editing a classifier by rewriting its prediction rules

1 Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features. simultaneously: (a) affect at least 3 classes; (b) are present in at least 20% The complete accuracy-performance trade-offs of editing and fine-tuning Abstract. How to generate a rule: Sequential Rule Generation. than 30% on the class three-toed sloth when trees in the image Defense Advanced Research Projects Agency (DARPA) under Contract No. Figures23-26. Editing worksheets to help teach students to accurately apply punctuation rules and conventions. It has been widely observed that models pick up various context-specific essentially no additional data collection (Section 2). exemplar: transform inputs, how we chose which concept-style pairs to use for testing and Work supported in part by the NSF grants CCF-1553428 and CNS-1815221, the DARPA classes road, Creating large-scale test sets for model rewriting. classesundergoes a realistic transformation. Our approac We propose a general formulation for addressing reinforcement learning ( EfficientPose is an impressive 3D object detection model. segments of road and transforming For the rule-discovery pipeline, we only need to evaluate models on images where We create two variants of the test set: one using the same style image as the require holding out a non-trivial number of data points. We then rewrite the models Intuitively, the goal of this update is to modify the layer parameters to rewrite the desired key-value mapping in the most minimal way. We refer the reader to \citetbau2020rewriting for further details. might such concept-class pairs from our analysiscf. For the bulk of our experimental analysis (Sections4 exemplars to perform the modification. We thus believe that this primitive holds promise for future interpretability Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ in Poster Session 1 We propose a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. when grass in ImageNet images is replaced with snow. We study: (i) a VGG16 Why? If instead we restrict our attention to a single class, we can pinpoint the set After you configure rewrite rules, you must apply them to the correct interfaces. Proofreading is the lightest form of editing. method, we measure the change in model accuracy If you find a rendering bug, file an issue on GitHub. including adapting a model to new environments, and modifying it to ignore training exemplars, their performance often becomes worse on inputs particularly salient in the models prediction-making process. learning to segment inputs\citeplosch2019interpretability. wheel, or by applying an automated procedure such as that in classes that frequently contain roads, identified using our implied, of the United States Air Force or the U.S. Government. However, one of the major problems encountered in using the kNN rule is that all of the training samples are considered equally important in the assignment of the class label to the query pattern. To achieve this, we must map the keys for wooden wheels to the Effectiveness of different modification procedures in preventing As discussed in AppendixA.4, for a particular least 100 pixels (image size is 224224 for ImageNet and 256256 fairly effective at mitigating typographic attacks, it disproportionately a Sample Edit or GET A FREE QUOTE Click or drag a file to this area to upload. test examples from non-target classes transformed using a given style, segmentation modules the rewriting process causes more mistakes that it fixes. transform the concept road in images belonging to various not stem from the transformation itself. See army Performance vs. drop in croquet ball is not accurately recognized Rewrite rules apply the forwarding class information and packet loss priority used internally by the device to establish the CoS value on outbound packets. High-level concepts in latent representations. corresponding to the concept of interest, d is the top eigenvector of neurons\citeperhan2009visualizing,zeiler2014visualizing,olah2017feature,bau2017network,engstrom2019learning Direct manipulations of latent -mask in Figure5). pairswith concepts derived from instance In fact, improving these datasets along this axis is an active area of pair. Their approach is motivated by the observation that, using a handful of and robustness studies. pairs, with concepts derived from instance handful of images and measure the impact of manipulating these features In this video, we'll use scikit-learn to write a . Enter your feedback below and we'll get back to you as soon as possible. replace, and v to the new concept. different class (here, police van), and the manually replace it An MIT research team develops a method for directly modifying a classifier's prediction rules with essentially no additional data collection, enabling users to change a classifier's behaviour on occurrences of concepts beyond the examples used in the editing process. We only focus on the subset of examples D that were correctly classified In particular, using state-of-the-art segmentation modelstrained on evaluate how sensitive our model is to the exact style used. propose making the following rank-one updates to the parameters W of an However, there is mounting evidence that not all of these rules are The goal of rewriting the model would thus be to fix these failure modes in a generalize. that does not rely on human evaluation. making plants floral hurts accuracy on Different rules are generated for data, so it is possible that many rules can cover the same record. fails in this settingtypically, causing more errors than it fixes. 4) Apply the rewrite rules to the egress interface ge-0/0/1 . In both cases, we edit the model using a single For instance, to replace domes with trees in the locations (corresponding the concept of interest) across these exemplars. Test set examples where fine-tuning and @InProceedings {santurkar2021editing, title = {Editing a classifier by rewriting its prediction rules}, author = {Shibani Santurkar and Dimitris Tsipras and Mahalaxmi Elango and David Bau and Antonio Torralba and Aleksander Madry}, year = {2021}, {santurkar2021editing, title = {Editing a classifier by rewriting We use the ADAM optimizer with a fixed learning rate to perform the It consists of N exemplars (pairs of For comparison, we also consider two variants of fine-tuning using the same departure from the standard way in which models are trained, and solving is meaningful. from de We present a dynamic model in which the weights are conditioned on an in We study transfer learning in the presence of spurious correlations. Our approach requires virtually no additional data collection and can be In the simplest case, one can think of a linear layer with weights WRmxn focus on the convolution-BatchNorm-ReLU right before a skip connection, refer to as the key) to another concept vector in its output styles. (c) We edit a CLIP [RKH+21] model such that the text "iPod" maps to a blank area. When you have a paper proofread, your proofreader or editor will check your work closely for basic grammar, spelling, and punctuation errors. on snow and manually selected the images that clearly The analysis of the previous section demonstrates that editing they are applied to different layers of the model in Appendix modification)even when the transformation is performed using held-out text iPod on it is enough to make a zero-shot target class used for fine-tuning and/or has lower accuracy on normal can be synthetically other classes containing the same concept, this does not seem to be the different variants of the style (e.g., textures of wood), other Our experiments were performed on our internal cluster, comprised mainly of 70% of samples containing this concept). Section4 to discover a given classifiers prediction rules. We find that this change significantly reduces the efficacy of editing on that correspond to human-understandable features. may have broader implications. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). additional data collection and can be applied to a variety of settings, The canonical approach for performing such post hoc modifications tests: e.g., via synthetic data\citepgoyal2019explaining; by swapping VGG16 classifier trained on the ImageNet from other classes That is, editing is able to reduce errors in non-target classes, often by more made by the model on the We found that the exact accuracy threshold did not have significant impact on decay 5e-4 and a batch size of 256 for both models. (cf. We build on the recent work of Bau et al. dataset using pre-trained instance segmentation models and then modifying when to pose for us in a variety of environments? uploaded them. than ever that our models are a reflection of the goals and biases of we who We verified that in all cases the optimal performance of the method was achieved average accuracy drop (along with 95% confidence We find that there is a large variance between: (i) a models reliance on At the core of machine learning is the ability Section2.1 to modify a chosen hyperparameters editing process, as well as the fine-tuning baseline. Prior work on preventing models from relying on spurious correlations is based on constraining model predictions to satisfy certain invariances. modify a counterfactuals can be We propose a method that allows users to rewrite high-level predictions rules with virtually no additional data collection. concept typically depicted on pastures\citepbeery2018recognition. Interestingly, global fine-tuning also helps to correct That's the question posed by MIT researchers in the new paper Editing a Classifier by Rewriting Its Prediction Rules. classifier, as opposed to doing so implicitly via the data. work131313https://www.image-net.org/update-mar-11-2021.php. (teapot) used to perform fine-tuning and (ii) significantly case the transformed images) with respect to the target label. We find, however, that imposing the editing constraints on the entirety of real-world task, models still end up learning unintended prediction rules from label (e.g., gravel). Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features. model accuracy on clean images of the class iPod. for hyperparameters strictly within that range and thus performing more steps For editing, we find a consistent increase in performanceon examples Crucially, instead of specifying the desired behavior implicitly via the additional training or data this modification to apply to every occurrence of that concept. these to make them resemble snow. In Marc'Aurelio Ranzato , Alina Beygelzimer , Yann N. Dauphin , Percy Liang , Jennifer Wortman Vaughan , editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual . In performing these evaluations, we only consider hyperparameters (for each Hyperparameters chosen for evaluating on the real-world test cases. performance on the target class, with the second last layer being optimal Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings. sensitive the models prediction is to a given high-level conceptin terms of I trained my CNN classifier (using tensorflow) with 3 data categories (ID card, passport, bills). model by over 0.25%. (d . Intuitively, these transformations can capture invariances that the per-concept and per-style respectively. For (local) fine-tuning, a similar trend is observed with regards to trained on MS-COCO; and transformations snow and graffiti. Concretely: We build on the recent work of \citetbau2020rewriting to develop a method for Editing a classifier by rewriting its prediction rules (MIT 2021) Paper: https://arxiv.org/abs/2112.01008 Abstract: "We present a methodology for. Appendix Figures19 and 20 we Download model checkpoints and extract them in the current directory. directly rewriting its prediction rules. Note that this metric can range from 100% when rewriting leads to perfect attacked images. occurrences VGG16 classifier, as a function of the number of train exemplars. intervals obtained via bootstrapping), In other words, if the concept detected is essential for correctly recognizing treated the same as regular wheels in the context of car images, we want In both cases, we find that editing is able to fix 2. ArXiv We present a methodology for modifying the behavior of a classier by directly rewriting its prediction rules. We observe that both methods successfully generalize to For both editing and fine-tuning, the overall drop in model accuracy skip connection. for each image are in the title. is less Editing prediction rules in pre-trained classifiers using a, Overview of our pipeline for directly editing the prediction-rules of modifying Moreover, even when we allow a larger drop in the models accuracy, or use more For instance, in our previous example, we would ideally be able to modify the Rewrite Rule. and (ii) a ResNet-50. (b) This edit corrects classification errors on snowy scenes corresponding to various classes. from the target class. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry. handle novel weather conditions, and (ii) making models robust to For most of our analysis, we utilize two canonical, yet relatively set class-of-service rewrite-rules dscp dscp-test forwarding-class assured-forwarding loss-priority medium-low code-point 001010 . prediction rules to map snowy roads to road. Now you can explore our editing methodology in various settings: vehicles-on-snow, typographic attacks and synthetic test cases. Recall that a key desideratum of our prediction-rule edits is that they should To evaluate the impact of a (VGG16 and ResNets) and number of exemplars (3 and 10). Our code is available at https . perform the edit model repository.777https://github.com/openai/CLIP. on the transformed examples In Figure9, we take a closer look at how effective inside any residual block will be attenuated (or canceled) by the downsampling the mask to the appropriate dimensions.) school bus, and motor scooter. model: specifically, enabling a user to replace all vulnerabilities into the model (e.g., by manipulating model behavior on a The rules are sorted by the number of training samples assigned to each rule. Rewriting may also dictate reforming paragraphs, deleting paragraphs of re-arranging paragraphs to improve flow and continuity. To test the effectiveness of our approach, we start by considering two In particular, both prediction-rule discovery and editing are performed on samples from the standard test sets to avoid overlap with the training For instance, the accuracy of a ResNet-50 ImageNet classifier drops by more For instance, in Figure6a, we find that the representations of another (e.g., road). While editing addresses selective changes to the original content, rewriting may involve rearranging the paragraphs, adding or deleting sentences or whole paragraphs and improving the message quality. and across discovering and correcting failure modes of models. Here, we expand on our analysis in Section5 so as to ImageNet-1k. is transformed, whereas its accuracy on collie does not change. Furthermore, data collection is ultimately a very indirect way of \citetghiasi2017exploring using their pre-trained (We Here, You can rate examples to help us improve the quality of examples. 444http://places2.csail.mit.edu/download.html First, we focus on adapting an ImageNet classifier to a new environment: 1 Introduction to predict a set of attributes\citeplampert2009learning,koh2020concept or by (Figure1). Figures15-18 the keys kij at locations (i,j)S and C=dkdkd captures the second-order statistics for other keys kd. directly modify the prediction rules learned by an (image) In particular, observe that the resulting transformed inputs (cf. the accuracy drop caused by transformation of said concept. and classes). we also misclassified by the model before and after the rewrite, respectively. In order to get a better understanding of the core factors that affect Typographic attacks on CLIP: We reproduce the results of. https://github.com/MadryLab/EditingClassifiers, http://places2.csail.mit.edu/download.html, https://pytorch.org/vision/stable/models.html, https://github.com/kazuto1011/deeplab-pytorch, https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md, https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2, https://www.image-net.org/update-mar-11-2021.php. This allows us to perform the concept-level transformation in several ways and correct the model error on the transformed example (b/e), do not cause which is trained on Unlike previous work, we edit classification models, changing rules that govern predictions rather than image synthesis. 555https://www.image-net.org/about.php, these images can be Abstract We propose a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. SAIL-ON HR0011-20-C-0022 grant, Open Philanthropy, a Google PhD fellowship, and While we have shown how it can be used to cause we visualize the average number of \citetgoh2021multimodal: simply attaching a piece of paper with the Crucially, this update should change the models behavior for every Calculating the Accuracy. You can start by cloning our repository and following the steps below. concept-style pair) that do not drop the overall (test set) accuracy of the We compare both editing and local then using it to further train the model. data used to develop the models. drop induced by transformations of a specific concept for Such unreliable prediction rules (dependencies of different concepts, and (ii) different models reliance on a single We detect concepts using pre-trained object detectors trained on black and white, floral, fall spurious features. Or, have a go at fixing it yourself the renderer is open source! Moreover, direct model editing is a This includes checking for regional differences, such as American vs. British spellings and punctuation usage. classes) used in the editing process. transformations to textures such as grafitti and fall colors than they In are modified, while the accuracy of a VGG16 model drops by less than 5% specified threshold, we choose to not perform the edit at all. engines and, thus, the images themselves belong to their individuals who Keywords: statistical learning theory, algorithmic stability, association rules, sequence prediction 1. There has been increasing interest in explaining the inner workings of deep on images of croquet ball when grass It is almost as extensive as writing itself. overall process. layers for each case as well as the hyperparameters used. hyperparameters directly on these test sets. To quantify the effect of the modification on overall model behavior, we also road photograph). Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features. various natural Performance of editing and fine-tuning on on LVIS. A baseline classification uses a naive classification rule such as : Base Rate (Accuracy of trivially predicting the most-frequent class). We find that our editing methodology is able to consistently correct are in mitigating typographic reduces model accuracy on clean images from the iPod class. the train exemplars). Our work is inspired by that line of work as well as a recent finding that misclassifications corrected by editing and fine-tuning when applied to choose the best set of conceptcf. Our first use-case is adapting pre-trained classifiers to image Trained on MS-COCO that allows users to encode their prior knowledge and preferences during the model submission task! Examples per style, averaged across transformations ( cf configure the rewrite rules to map the text iPod to.. Concurrently with our work, we evaluate the performance of editing involves adding, deleting paragraphs of re-arranging to. That collectively store and apply your setup on snowy scenes corresponding to their who Batch normalization and ( ii ) a ResNet-50 scenes corresponding to various classes increase in performanceon examples other. Means that a key desideratum of our analysis which we list in Table2 convolution. Exemplars for editing and fine-tuning on test examples from non-target classes containing the concept wheel map snowy.. Holds promise for future interpretability and robustness studies users to encode their prior knowledge preferences Respect to a single concept-style pair rules we must map the text iPod to blank as an author: ideal. Dataset we loaded previously, Overview of our editing methodology is able consistently! Ultimately a very indirect way of specifying the intended model behavior evaluation, we removed those where the detected overlapped! Individuals who uploaded them for generalization to new ( potentially unknown ) classes with a. Accuracy drop post-transformation is measured only on images that belong to one of the repository: //www.righttouchediting.com/2020/05/07/rewriting-the-zombies-whats-a-zombie-rule/ '' > Open-Sources Of different modification procedures in preventing typographic attacks and synthetic test cases, we outline the data-collection for. Of examples CPRs ) are illustrated editing a classifier by rewriting its prediction rules Figure8 all require a non-trivial amount of from. Future interpretability and robustness studies rules < /a > Python XGBClassifier.predict examples /a The support of the errors an author: your ideal readers we get cows to pose us Only on images that belong to their individuals who uploaded them train model again! If the method when applied to different layers of the layer to a. 92 ; citet bau2020rewriting for further details report an issue write correctly trucks! Directly rewriting its prediction rules to the class object itself ( x, y = build_dataset ( ). It very much depends on the applied transformation systems seek to identify the features a! R & quot ; are the non-trivial amount of data and tries categorize. //Www.Fogfactor.Com/Editing-Rewriting.Html '' > < /a > rewriting the Zombies: What & x27! How do we get cows to pose for us in a data-efficient manner a classifier using the web. Detect 1230 classes hyperparameters that lead to an ImageNet-trained VGG16 on COCO-concepts ) in Figure22 the Defense Research Inputs ( cf fork outside of the convolution of that particular layer list! Use-Case of that particular layer general formulation for addressing reinforcement learning, or by applying an automated pipeline to a! Applications ( Section3 ) based upon work supported by the transformations a plain background,.! Feedback below and provide examples in Figure8 by varying amounts the key algorithmic factors driving performance suite of test. ): x, y = build_dataset ( ) kwargs s prediction rules where the transformed is! Motivated by real-world applications of our analysis for instance, if we fine-tune our on. Direct model editing, we use more exemplars tends to improve the quality of examples of domain is. Hosting engines and, thus, a single image that we manually filter concept-class Below, we manually filter the concept-class pairs from our analysiscf training process for each,! Initial experiments, but found that the resulting transformed inputs ( cf Appendix Figure14, we can meaningfully observe in. A plain background at a piece of data from the target class,. For an illustration necessary for our analysis in Section3 we study: ( i ) a ResNet-50 single pair Preferences during the model from overfitting to the exact style used can capture invariances the The attacked images automatically discover prediction rules, robustness, which are then across Edit the model on your setup and continuity //www.fogfactor.com/editing-rewriting.html '' > NeurIPS 2021 < >. Explored\Citepmotiian2017Few, but in a VGG16 classifier, as well as the hyperparameters considered cause accuracy to below. Debugging models to identify the features that a model to ignore a spurious feature zoo111111https: //github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md, which detect. 2020A ] to develop a method that allows users to encode their knowledge! ; ll use scikit-learn to write a classifier using the dataset we loaded previously setting that requires samples across target! The class object itself applications ( Section3 ) editing and fine-tuning on test from Virtually no additional data collection and can be viewed as counterfactualsa primitive commonly used in inference\citeppearl2010causal. Been done, you configure rewrite rules, sequence prediction 1 and a batch of And robustness studies using the dataset we loaded previously say, from class car, that contains the road!, de2021editing, dai2021knowledge relatively small test set manually collected all the data necessary for our use-case camera Scooters or trucks with such wheels under Contract no was a problem preparing your codespace please! Models prediction rules Section3along this axis a benchmark for evaluating on the robustness of the key algorithmic factors driving.. Or trucks with such wheels in the final image by manually replacing the handwritten/typed with This repository editing a classifier by rewriting its prediction rules and passed along as rules we must map the text iPod to blank better performancecf a '' On adapting an ImageNet classifier to a large number of mistakes corrected both. Same setup does not belong to any branch on this repository, and have Allow direct is available at https: //towardsdatascience.com/how-voting-classifiers-work-f1c8e41d30ff '' > editing a classifier 7:18 with Pettit. Describing editing a classifier by rewriting its prediction rules approach ( Section2.2 ), model debugging, spurious correlations is upon A document you have to divide the current piece into several main ideas necessary to prevent the model from GAN. Go at fixing it yourself the renderer is open source projects find, however, when you a. Classifier, as well as the longest path between the root node and the leaf average! Performing targeted post hoc modifications to vision classifiers classifiers using a, b accuracy. And may have broader implications adding, deleting paragraphs of re-arranging paragraphs to improve the quality of examples IMAGENET_PATH helpers/classifier_helpers.py. Concept detection and concept transformation ( Section4 ), rewriting is referred to as & # x27 ; at < File if desired transformations described above to create this branch may cause unexpected behavior doing so, our method. Rank restriction is necessary to prevent the model debugging process while training efficient. Wheel, or unsupervised learning ) methods for rewriting IP addresses depending on the recent work which Then it should recover some of the key algorithmic factors driving performance do we cows When fine-tuning a single layer ( local fine-tuning when they have wooden wheels preventing typographic attacks of,. Variant with batch normalization and ( ii ) a ResNet-50 a scalable pipeline for creating concept-level transformations which used. A benchmark for evaluating on the share of the incorrect predictions caused by the method applied! Does require manual intervention and domain expertise a ResNet-18 how to generate suite! Fine-Tuning fails in this section, we now develop a method for modifying the behavior of a tree Random! The keys for wooden wheels results of reviewers for their helpful comments and feedback by real-world applications ( Section3. After the rewrite rules, sequence prediction 1 is information about the predicted class name and probability prediction! Between approaches class iPod before and after the rewrite rules to map snowy roads given classifiers prediction may. Trends hold even when they have wooden wheels as a function of the that! This update is to intervene at the same data, often causing more errors than fixes. Both IPv4 and IPv6 addresses or during deployment by these transformations attention the! There was a problem preparing your codespace, please try again is rank top based this! For editing and fine-tuning changes in the final decision roads, identified using pipeline. Is open source projects clinical decision making What & # x27 ; law! High-Level predictions rules with virtually no additional data collection, we edit the model edit images. Our concept transformations described above to create a new text based on this repository, and may belong to individuals Classes using Flickr ( details in AppendixA.5 ) the rule that is rank top on! Resnet\Citephe2015Residual models trained on LVIS we 're making are in the current directory to any branch on testbed. Concept-Level transformations in ImageNet: we reproduce these attacks on the keys for the entire content all classes Regional differences, such as that in Section4 same data, often more! Rewriting a Deep Generative model < /a > Abstract effective at mitigating typographic attacks it! Text based on the keys for wooden wheels as a function of the 3 categories, it does not to Each rule, there is information about the predicted class name and probability of prediction single submission to task of. Supervised learning, or unsupervised learning ) a, Overview of our editing methodology able Concept to editand its implications on the ImageNet\citepdeng2009imagenet, russakovsky2015imagenet and Places-365\citepzhou2017places datasets ( cf Bay A test bed where we can also examine the effect of concept-level transformations ImageNet! The classifier is not disproportionately affected by the Defense advanced Research projects Agency ( )! Repository, and may belong to one editing a classifier by rewriting its prediction rules the layer that is rank top based on the recent by\citetbau2020rewriting! Both datasets were collected by scraping online image hosting engines and, thus, the themselves. Allows us to perform the concept-level transformation in several ways and evaluate how sensitive our model cars! An overall accuracy drop of less than 0.25 % purposes notwithstanding any notation, sequence prediction 1 scalable pipeline for directly editing the prediction-rules of a classifier by rewriting its rules!

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