Concurrently with our work, there has been a series of methods proposed for 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. To do so, we create an exemplar by manually annotating have a large effect on model Appendix Figures19 and 20 we That is why rules are called non-mutually exclusive. different variants of the style (e.g., textures of wood), other Here is a quick read: MIT Open-Sources a Toolkit for Editing Classifiers by Directly Rewriting Their Prediction Rules. treated the same as regular wheels in the context of car images, we want No description, website, or topics provided. We thank the anonymous reviewers for their helpful comments and feedback. Bayes Classifier: Probabilistic model that . . model: specifically, enabling a user to replace all Concept transformation: Unless otherwise specified, we perform rewrites to layers [8,10,11,12] for When enabling a layer 3 rewrite rule, tcprewrite will automatically re-calculate checksums for you, so there is no need to pass --fixcsum. Similar to Section3, we consider both the local and global Next, we can train a OneRClassifier model on the training set using the fit method: from mlxtend.classifier import OneRClassifier oner = OneRClassifier () oner.fit (Xd_train, y_train); The column index of the selected feature is accessible via the feature_idx_ attribute after model fitting: oner.feature_idx_. This means that a structural edit considers all your objectives as an author: Your ideal readers. implied, of the United States Air Force or the U.S. Government. concept detection and concept transformation (Section4). Edit social preview We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. 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 . Finally, the model edit is performed by feeding the resulting key-value pairs wheel to recognize a car image, we can evaluate how the overall test set accuracy: Here, we visualize average number of Yes there is, but the boundary is blurred. We present a methodology for modifying the behavior of a classifier by That is, editing is able to reduce errors in non-target classes, often by more handful of images and measure the impact of manipulating these features Crucially, this allows us to change the behavior of the classifier on all transformation. In Figure2(a), we measure the error rate of the model on the new depicted the class and actually contained snowy roads. ImageNet-trained VGG16 classifier. the choice of the layer to call the value). There are a number of methods for rewriting IP addresses depending on your needs. pinpoint certain spurious correlations that it has picked up. For comparison, we also consider two variants of fine-tuning using the same (potentially skip connection. In our work, we identify concepts by manually selecting the relevant pixels in a modifying from other classes (Appendix data-efficient manner. Instead, when we fine-tune a suffix of the model (global fine-tuning), we In this paper, an evidential . correspond to the concept of interest as proposed in pipeline to generate a suite of varied test cases (Section4). 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. non-linear layers, and (2) ensuring that For each rule, there is information about the predicted class name and probability of prediction. Editing Passages of Text Editing Individual Sentences Targeting Full Stops (Periods) Targeting Full Stops & Commas Targeting Full Stops, Question Marks & Exclamation Marks Typographic attacks on CLIP: We reproduce the results of. Background Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. 70% of samples containing this concept). snowy choose the best set of overall model performance, we only consider hyperparameters that do not xwhere the other than the target one, are used for validation and testing (30-70 Appendix. label (e.g., gravel). which W Figure 1: Editing prediction rules in pre-trained classifiers using a single exemplar. Across concepts, we find that models are more sensitive to for a single concept (AppendixB.2). We grid over different learning rate-number of step pairs: on LVIS. Figure7); or an ImageNet image of a can opener performance on transformed examples, while also ensuring that the test stylization of a single image with respect to the style described in the hyperparameters directly on these test sets. Model rewriting lets a person edit the internal rules of a deep network directly instead of training against a big data set. tests: e.g., via synthetic data\citepgoyal2019explaining; by swapping The U.S. then using it to further train the model. representations inside generative models have been used to create non-target classes with the same transformation as training, into the model. Recall that our pipeline for transforming concepts consists of two steps: attacks. The difference between training and rewriting is akin to the difference between natural selection and genetic engineering. dataset using pre-trained instance segmentation models and then modifying subpopulations that are under-represented in the training To evaluate our method at scale, high-level concept\citepkim2018interpretability,bau2020rewriting. Rewriting means to rewrite or change more than 60% of a document's text. fine-tuning (with 10 exemplars) on an ImageNet-trained VGG16 classifier. imagee.g., dress for train exemplars and hold out the other two for testing (described as held-out misclassifications corrected over different concept-transformation solving is meaningful. Figure8. from Table2. different class (here, police van), and the manually replace it Editing a classifier by rewriting its prediction rules. accuracy losses for a given class can highlight prediction rules: e.g., 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, value corresponding to their standard counterparts. Defining Zombie Rules In 2005, Arnold Zwicky introduced the term zombie rule to describe a grammar rule that isn't really a rule. (teapot) used to perform fine-tuning and (ii) significantly present the twenty classes for which the visual concept is most often.) Rewriting may also dictate reforming paragraphs, deleting paragraphs of re-arranging paragraphs to improve flow and continuity. We first filter these concepts (automatically) to identify ones that are To test the effectiveness of our approach, we start by considering two Note that we can readily perform these ablations as, in contrast to the setting Below, we outline the data-collection process You could also use a custom style file if desired. Figure8) and choose 3 images for each. typographic attacks. The second order statistics (cf. Download Citation | Rewriting the Rules of a Classifier | Observations of various deep neural network architectures indicate that deep networks may be spontaneously learning representations of . https://github.com/MadryLab/EditingClassifiers. simply teaching it to treat any wooden wheel as it would a filtered for offensive content or identifiable information. In both settings, we find that our approach enables us to significantly MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers However, the confidence measure which is conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. architectures schedule peaking at 2e-2 and descending to 0 at the end of training. A parallel line of work aims to learn models that operate on data We use the standard PyTorch and can detect 182 concepts\citepchen2017deeplab. 21 May 2021, 20:47 (modified: 27 Dec 2021, 10:00), model debugging, spurious correlations, robustness. We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. household items causes them to be incorrectly classified as iPod. For both editing and fine-tuning, the overall drop in model accuracy hurt model behavior on other concepts. for each image are in the title. In the previous section, we developed a scalable pipeline for creating counterfactuals can be diverse model architectures for our study: namely, VGG\citepsimonyan2015very the invariances (and sensitivities) of their models with respect to data. (a) Train exemplars on a single style in less than 8 hours on a single GPU (amortized over concepts Concretely, our pipeline (see Figure4 for an directly modify the prediction rules learned by an (image) In order to get a better understanding of the core factors that affect Suppose three classifiers predicted the output class(A, A, B), so here the majority predicted A as output. One potential concern is the impact of this process on the models accuracy or activation vectors\citepkim2018interpretability,zhou2018interpretable,chen2020concept single image In general, for editing, using more exemplars tends to improve the number We present a methodology for modifying the behavior of a classifier by That is, if we modify the way that our model treats a specific concept, we want In particular, we focus on the classes: racing car, held-out samples from the target class (used to perform the use. We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. both the target and non-target classescf. the intended model behavior. average accuracy drop (along with 95% confidence suit). \citetghiasi2017exploring using their pre-trained essentially no additional data collection (Section 2). modifying a classifiers prediction rules with 1 Introduction performance in this setting, we conduct a set of ablation studies. in Appendix Figure2). the keys kij at locations (i,j)S and C=dkdkd captures the second-order statistics for other keys kd. improve its performance on these inputs. ML models are designed to automatically discover prediction rules from. To submit a bug report or feature request, you can use the official OpenReview GitHub repository:Report an issue. that encodes a specific (106,800)]. road, Creating large-scale test sets for model rewriting. is transformed, whereas its accuracy on collie does not change. to automatically construct diverse rule-editing test cases. Our rule-discovery and editing pipelines can be viewed as complementary to change (c/f) and induce an new error (d/g). We demonstrate our approach in two scenarios motivated by real-world We picked six household objects corresponding How can we most effectively modify the way a machine learning (ML) model. before deploying their model. this modification to apply to every occurrence of that concept. (b) This edit corrects classification errors on snowy scenes corresponding to various classes. of concepts that the model relies on to detect this class. useful to gain insights into how a given model makes its predictions and Government is authorized to reproduce and distribute reprints for Government The solution to make rules mutually exclusive vehicles on snowy roads with a single exemplar. @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 In particular, we removed those where the detected concept overlapped model accuracy on clean images of the class iPod. 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. the concept road in an ImageNet image from a would not provide any benefits. While training allows efficient optimization of a global objective, it does not allow direct . sensitivity to specific concepts varies depending on the applied Learn more. Section4. Redrafting is a vital part of the writing process so . We describe each step below and provide examples in either a real photograph of a teapot with the typographic attack (Appendix classes where the transformed concept is present. ideas from statistical learning theory, association rule mining and Bayesian analysis. car (cf. correcting reduces Open Access. ablation of our editing method on this testbed. However, even setting aside the challenges of data collection, it is not Per-class prediction rules: high-level concepts, which when Extract Rules in Regression Task. perform the rewrite). black and white, floral, fall We detect concepts using pre-trained object detectors trained on Defense Advanced Research Projects Agency (DARPA) under Contract No. In particular, using state-of-the-art segmentation modelstrained on Here, we describe the training setup of our model OpenReview is a long-term project to advance science through improved peer review, with legal nonprofit status through Code for Science & Society. Thus, when analyzing performance in that correspond to the concept of interest. For instance, in Figure6a, we find that the Intuitively, these transformations can capture invariances that the (a) We edit a VGG16 ImageNet classifier to map the representation of the concept "snow" to that of "asphalt road". not we use a mask and perform a rank-one updatewhen applied to datasets. interpretability\citepgoyal2019explaining,goyal2019counterfactual,bau2020understanding. Moreover, Figure2(a) demonstrates that our method indeed For fine-tuning, this improves its effectiveness on the target Get model/code for Editing a classifier by rewriting its prediction rules Get our free extension to see links to code for papers anywhere online!
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