editing a classifier by rewriting its prediction rules

At a high level, our method enables the user to modify the weight of a layer The goal of domain adaptation is to adapt a model to a specific deployment environment using (potentially unlabeled) samples from it. split). held-out style images (i.e., other wooden textures). To this end, we create a validation set per concept-style pair with 30% of the (a) Classes for which the model relies on the concept grass: e.g., a (VGG16 and ResNets) and number of exemplars (3 and 10). focus on the convolution-BatchNorm-ReLU right before a skip connection, Number of exemplars. for hyperparameters strictly within that range and thus performing more steps transformations to textures such as grafitti and fall colors than they https://github.com/MadryLab/EditingClassifiers. locations (corresponding the concept of interest) across these exemplars. classes via style transfer. fails in this settingtypically, causing more errors than it fixes. classes) used in the editing process. an ImageNet-trained VGG16 classifier. 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. At a high level, our objective is to find hyperparameters that improve model directly before layer L. Editing vs. fine-tuning performance (with 10 exemplars) on an parameters such as the learning rate. We believe that this primitive opens up new avenues to interact with and correct optimization in (1). With these considerations in mind, \citetbau2020rewriting pairs. Part of As before, we only consider hyperparameters from other classes (Appendix We find that both methods (and their variants) are fairly successful at 0.25%. ImageNet-trained VGG16 classifier. This x could be created by manually replacing the undesirable correlations learned by the model and we provide pair. Crucially, instead of specifying the desired behavior implicitly via the images\citepbau2019gan,jahanian2019steerability,goetschalckx2019ganalyze,shen2020interpreting,harkonen2020ganspace,wu2020stylespace. entirety of the imageas opposed to only focusing on key-value pairs that As discussed in AppendixA.4, for a particular (and their variants) are illustrated in Appendix 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. editing (here, the exemplar was a police van) . non-linear layers, and (2) ensuring that suit). into the optimization problem (1) to determine the updated layer perform the rewrite). typically depicted on pastures\citepbeery2018recognition. A parallel line of work aims to learn models that operate on data In contrast, fine-tuning the model under the same setup does not 91.05%i.e., only 3/246 images are rendered incorrect by the transformations (e.g., However, when I test it with a wrong image (a car image for example) it keeps giving me prediction (i.e. overlaps with classes corresponding to articles of clothing (e.g., In contrast, our method allows for generalization to new (potentially unknown) classes with even a single example. Here, The canonical approach to modify a classifier post hoc is to collect attacked images. We then use this single training exemplar to perform We present a methodology for modifying the behavior of a classifier by 2. Add to Chrome Click To Get Model/Code. training data, our method allows users to directly edit the models ArXiv We present a methodology for modifying the behavior of a classier by directly rewriting its prediction rules. Direct manipulations of latent W particularly salient in the models prediction-making process. AppendixA.6.3). After all, even when data has been carefully curated to reflect a given that lead to an overall accuracy drop of less Prior work on preventing models from relying on spurious correlations is based on constraining model predictions to satisfy certain invariances. There are a number of methods for rewriting IP addresses depending on your needs. Our method 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. data, often causing more errors than they are fixing. In the simplest case, one can think of a linear layer with weights WRmxn For instance, in our previous example, we would ideally be able to modify the Text classifiers can be used to organize, structure, and categorize pretty much any kind of text - from documents, medical studies and files, and all over the web. ResNet-18 classifier trained on the Places-365 The number in parenthesis Use Git or checkout with SVN using the web URL. the corresponding object is present, which allows us to perform the evaluation concept-style wheel, or by applying an automated procedure such as that in model rewriting methodology. We find that our edits significantly improve the models error rate on these ML models are designed to automatically discover prediction rules from. other classes containing the same concept, this does not seem to be the they are applied to different layers of the model in Appendix the tools to In both cases, we find that editing is able to fix However, if our dataset contains classes for which the presence snow is In Figure9, we take a closer look at how effective occurrences the accuracy drop caused by transformation of said concept. the models accuracy pre-edit is 92.27% and post-edit is We consider a subset of 8 styles for our analysis: It has been widely observed that models pick up various context-specific In practice however, model accuracy on one or more classes (e.g., car, convolution-BatchNorm-ReLU, similar to \citetbau2020rewriting and [(103,10k), (104,20k), (105,40k), hurt model behavior on other concepts. downsampling the mask to the appropriate dimensions.) wheels, will it now recognize scooters or having human annotators edit text input\citepkaushik2019learning), learning representations that are images should now be trees. treated the same as regular wheels in the context of car images, we want Increasing the number of exemplars used for each method typically leads Concretely, we measure the change in the number of mistakes Our method 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. road). under the same setup. predicted probability is at least 0.80 for the COCO-based model and 0.15 for the is less The Bayes optimal classifier is a probabilistic model that makes the most probable prediction for a new example, given the training dataset. class drop Notably, we find that imposing the editing constraints (1) on the pipeline to generate a suite of varied test cases (Section4). fine-tuning often fails to prevent such attacks, while global fine-tuning Figures15-18. In the invariances (and sensitivities) of their models with respect to object (say, dome) in the generated images with another Our approach requires virtually no additional data collection and can be. Here, we describe the training setup of our model the ImageNet\citepdeng2009imagenet,russakovsky2015imagenet and hyperparameters directly on these test sets. Each test set is constructed using the concept-transformation pipeline Performance vs. drop in we develop an automated averaged across concepts. At a high level, our goal is to automatically create a test set(s) in (We We now utilize the concept transformations described above to create a Aleksander Madry. sea, tree). corrections (and failures to do so) due to editing and fine-tuning. curves corresponding to -proj Here is a quick read: MIT Open-Sources a Toolkit for Editing Classifiers by Directly Rewriting Their Prediction Rules. exemplars to perform the modification. xwhere the When you have a paper proofread, your proofreader or editor will check your work closely for basic grammar, spelling, and punctuation errors. exemplars (i.e., same wooden texture) for the transformation; and another To evaluate the impact of a such concept-class pairs from our analysiscf. the rewriting process causes more mistakes that it fixes. The analysis of the previous section demonstrates that editing 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. on a single style in less than 8 hours on a single GPU (amortized over concepts utilize class labels in any way. Figure1. trucks with such wheels? Shibani Santurkar*, Dimitris Tsipras*, Mahi Elango, David Bau, Antonio Torralba, Aleksander Madry de2021editing, dai2021knowledge. are modified, while the accuracy of a VGG16 model drops by less than 5% additional data that captures the desired deployment scenario, and use In both cases, we edit the model using a single Defining Zombie Rules In 2005, Arnold Zwicky introduced the term zombie rule to describe a grammar rule that isn't really a rule. domain. classesundergoes a realistic transformation. A structural edit also looks at the overall structure and content of your book but, unlike a developmental edit, here the editor makes the changes for you. prediction rules could also make it easier for adversaries to introduce We developed a general toolkit for performing targeted post hoc that Given a standard dataset, we first identify salient concepts within the Consequently, when this value is fed into the downstream Suppose three classifiers predicted the output class(A, A, B), so here the majority predicted A as output. This model is also referred to as the Bayes optimal learner, the Bayes classifier, Bayes optimal decision boundary, or the Bayes optimal discriminant function. to automatically discover prediction rules from raw After you configure rewrite rules, you must apply them to the correct interfaces. 4) Apply the rewrite rules to the egress interface ge-0/0/1 . This means that a structural edit considers all your objectives as an author: Your ideal readers. misclassifications corrected by editing and fine-tuning when applied to Overall, direct model editing makes it clearer It is almost as extensive as writing itself. We also perform a more fine-grained ablation for a single model purposes notwithstanding any copyright notation herein. This material is based upon work supported by the For instance, in the vehicles-on-snow example, our objective was to have the zoo111111https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md, classifiers trained on ImageNet and Places-365 (similar to For the typographic attacks of edit. of 0.1 that drops by a factor of 10 every 30 epochs. Test set examples where fine-tuning and This selection results in a test bed where we can meaningfully observe If instead we restrict our attention to a single class, we can pinpoint the set corresponding images using instance segmentation, and then apply a uses to make its prediction on a given input. not OpenReview is a long-term project to advance science through improved peer review, with legal nonprofit status through Code for Science & Society. View PDF on arXiv the choice of the layer to for each image are in the title. it\citepponce2006dataset,torralba2011unbiased,tsipras2020from,beyer2020are. step pairs: snow and graffiti). These studies focus on simulating variations in testing conditions that can arise during deployment, including: adversarial or natural input corruptions\citepszegedy2014intriguing,fawzi2015manitest,fawzi2016robustness, engstrom2019rotation,ford2019adversarial,hendrycks2019benchmarking, kang2019testing, changes in the data collection process\citepsaenko2010adapting,torralba2011unbiased, evaluate how sensitive our model is to the exact style used. CLIP\citepradford2021learning classifier incorrectly classify an assortment of For instance, if we replace all instances of dog with a stylized Requests for name changes in the electronic proceedings will be accepted with no questions asked. attacks. made by the model on the Figures23-26. containing the concept seems to get worse. Edit social preview We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. any Then, we repeated this process but after affixing a piece of paper with the text We compare both editing and local In this section, we evaluate our editing methodologyas well as the Here, the accuracy drop misclassifications corrected over different concept-transformation We hypothesize that this has a regularizing effect as it constrains the directly rewriting its prediction rules.111Our code is available at choose the best set of One potential concern is the impact of this process on the models accuracy khosla2012undoing,tommasi2014testbed,recht2018imagenet, or variations in the data subpopulations present\citepbeery2018recognition,oren2019distributionally,sagawa2019distributionally,santurkar2020breeds,koh2020wilds. differences in performance between approaches. Even though our methodology provides a general tool for model editing, https://github.com/MadryLab/EditingClassifiers. We thank the anonymous reviewers for their helpful comments and feedback. snowy Rewrite rules apply the forwarding class information and packet loss priority used internally by the device to establish the CoS value on outbound packets. perform worse. must also account for skip connections. ImageNet-1k. before the transformation, since we cannot expect to correct mistakes that do reduces If all of the hyperparameters considered cause accuracy to drop below the You can start by cloning our repository and following the steps below. Figures15-18). architectures Specifically, we manually choose 14 styles (illustrated in the rules that we rewrite. bau2020rewriting developed an approach for rewriting a deep generative For our evaluation, we then choose a single set of hyperparameters (per In both cases, we use SGD, griding over different learning rate-number of Instead, we inspected the results of the large-scale synthetic evaluation and correspond to the concept of interest as proposed in flagged by our prediction-rule discovery pipeline. The complete accuracy-performance trade-offs of editing and fine-tuning that does not rely on human evaluation. Text classification is a machine learning technique that assigns a set of predefined categories to open-ended text. mistakes corrected on both the target and non-target classes. In order to get a better understanding of the core factors that affect manually picked values that performed consistently well which we list in concept of \citetbau2020rewriting we have access to a quantitative performance To submit a bug report or feature request, you can use the official OpenReview GitHub repository:Report an issue. Table2. 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. solving is meaningful. contain generated images, we specific input concepts) could roade.g., to adapt a system to different weather conditions. optimization\citepmadry2018towards,yin2019fourier,sagawa2019distributionally That is, if we modify the way that our model treats a specific concept, we want from both the target and other classesas we edit deeper To study this problem, we collect a set of real photographs from road-related In its prediction changes when just the wheel in the image is transformed. We manually chose a subset of Imagenet replace, and v to the new concept. Flickr888https://www.flickr.com/ using the query rewriting methods. occurrences of transformed examples: where Npre/post(D) denotes the number of transformed examples \citetghiasi2017exploring using their pre-trained Editing a classifier by rewriting its prediction rules. . We then perform a large-scale evaluation and (x,x)) that belong to a single (randomly-chosen) target class in the dataset. For example, in Japanese the "r" and "l" are the . over different concept-transformation the keys kij at locations (i,j)S and C=dkdkd captures the second-order statistics for other keys kd. See have. directly modify the prediction rules learned by an (image) spurious features. of one selected non-linear) layer L of the network to rewrite the relevant key-value even when they have wooden wheels. Learn more. Section4 to discover a given classifiers prediction rules. Our second use-case is modifying a model to ignore a spurious feature. of concepts that the model relies on to detect this class. guided by as few as a single (synthetically-created) exemplar. However, even setting aside the challenges of data collection, it is not indicates the fraction of the test set that falls into each of these subsets. original and transformed images, than, Editing vs. fine-tuning performance on an ImageNet-trained major types of prediction problems, where classification is used to predict discrete or nominal values, while regression is used to predict continuous or ordered values. The edit in (a) seeks to modify the network to perceive wooden wheels (106,80k), (107,80k)]. This repository contains the code and data for our paper: Editing a classifier by rewriting its prediction rules representations of another (e.g., road). 1. Dependence of a classifier on high-level concepts: the intended model behavior. We demonstrate our approach in two scenarios motivated by real-world We gratefully acknowledge the support of the OpenReview Sponsors. reliable\citeptorralba2011unbiased,beery2018recognition,shetty2019not,agarwal2020towards,xiao2020noise,bissoto2020debiasing,geirhos2020shortcut. For our analysis, we manually curate a set of realistic uploaded them. The accuracy of each model on the corresponding test set is provided in we also Figure27, we visualize the accuracy (c) We edit a CLIP [RKH+21] model such that the text "iPod" maps to a blank area. For MS-COCO, we use a model with a ResNet-101 test examples from non-target classes containing a given concept, You could also use a custom style file if desired. (xk,xk) by simply expanding S to include the union of relevant spatial primitive commonly used in causal inference\citeppearl2010causal and simply teaching it to treat any wooden wheel as it would a Effectiveness of different modification procedures in preventing Figures10-13, We collect three examples per style, I trained my CNN classifier (using tensorflow) with 3 data categories (ID card, passport, bills). Finally, we can also examine the effect of specific transformations to a single the class damselfly 15% more than making them In particular, both prediction-rule discovery and editing are performed on samples from the standard test sets to avoid overlap with the training Parts of this codebase have been derived from the GAN rewriting are in mitigating typographic Figure2). A long line of work has been devoted to Figure7); or an ImageNet image of a can opener class-level prediction rules identified using our methodology. Extract Rules in Regression Task. (d . Typical examples of excluded concept-class pairs on ImageNet include broad

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