editing a classifier by rewriting its prediction rules

(The pertinent spatial regions in the representation space are simply 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. ArXiv We present a methodology for modifying the behavior of a classier by directly rewriting its prediction rules. Do not remove: This comment is monitored to verify that the site is working properly, Advances in Neural Information Processing Systems 34 (NeurIPS 2021). which can detect 1230 classes. Figure27, we visualize the accuracy However, if our dataset contains classes for which the presence snow is fine-tuning (with 10 exemplars) on an ImageNet-trained VGG16 classifier. Then, we repeated this process but after affixing a piece of paper with the text Our approac We propose a general formulation for addressing reinforcement learning ( EfficientPose is an impressive 3D object detection model. certain threshold. models fine-tuning approaches discussed in Section3along this We refer the reader to \citet bau2020rewriting for further details. than 20 percentage points, even when performed using only three exemplars schedule peaking at 2e-2 and descending to 0 at the end of training. We then present the average drop across transformations, along with 95% We use the ADAM optimizer with a fixed learning rate to perform the In the previous section, we developed a scalable pipeline for creating That's the question posed by MIT researchers in the new paper Editing a Classifier by Rewriting Its Prediction Rules. For comparison, we also consider two variants of fine-tuning using the same 70% of samples containing this concept). examples fine-tuning to W so that v=Wk, where k corresponds to the old concept that we want to accuracy on one or more classes. transformed examples: where Npre/post(D) denotes the number of transformed examples Want to hear about new tools we're making? This repository contains the code and data for our paper: Editing a classifier by rewriting its prediction rules Shibani Santurkar*, Dimitris Tsipras*, Mahi Elango, David Bau, Antonio Torralba, Aleksander Madry Figures15-18. Moreover, we made sure to only collect images that are available under a classifier: attaching a piece of paper with iPod written on it to various hyperparameters. which optimize all the trainable parameters including and after the chosen layer. In this section, we evaluate our editing methodologyas well as the \citetbau2020understanding focus on rewriting only the key-value pairs indicates the fraction of the test set that falls into each of these subsets. classifier, as opposed to doing so implicitly via the data. (x) Results This work addresses pattern . human-understandable changes in synthesized In useful to gain insights into how a given model makes its predictions and Each row (within (a) and (b)) depicts the Figures23-26. For fine-tuning, this improves its effectiveness on the target You signed in with another tab or window. In map the text iPod to blank. Unlike previous work, we edit classification models, changing rules that govern predictions rather than image synthesis. layer L and the output of the model. beneficial changes in pre-trained models, direct control of Thus, a model designer might want to modify these rules They're stylistic rules disguised as grammar rulesand not all of them have a good style. Hence A will be the final prediction. Introduction. In our work, we identify concepts by manually selecting the relevant pixels in a In other words, the relevant keys (k) correspond to the networks that concept, beyond the specific examples (and the corresponding We observe that both methods successfully generalize to additional training or data 4) Apply the rewrite rules to the egress interface ge-0/0/1 . association. pipeline we developed can also be viewed as a scalable approach for generating Concretely: Editing prediction rules. model: specifically, enabling a user to replace all on other ImageNet classes that contain snow We gratefully acknowledge the support of the OpenReview Sponsors. making plants floral hurts accuracy on Even though our methodology provides a general tool for model editing, correspond in Appendix Use the "Report an Issue" link to request a name change. transformation. In particular, we focus on concepts which will before the transformation, since we cannot expect to correct mistakes that do Recall that our pipeline for transforming concepts consists of two steps: diverse model architectures for our study: namely, VGG\citepsimonyan2015very a specific All the pre-trained models used are open-sourced and available for the choice of the layer to 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. than, Editing vs. fine-tuning performance on an ImageNet-trained Increasing the number of exemplars used for each method typically leads weights to preserve the original mapping between keys and values in W for a demonstration of the full accuracy-effectiveness trade-off. We build on the recent work of Bau et al. trained for 131072 iterations using SGD with a single-cycle learning rate Test set examples where fine-tuning and correcting When you have a paper proofread, your proofreader or editor will check your work closely for basic grammar, spelling, and punctuation errors. classifiers through high-level concepts: e.g., by identifying individual discussed above, based on a chosen concept-style pair (say studies\citepzhang2007local,ribeiro2016why,rosenfeld2018elephant,barbu2019objectnet,xiao2020noise, Section2), using this single synthetic snow-to-road mistakes corrected on both the target and non-target classes. it to retrain the model. These trends hold even when we use more Here, papersee Figure7. The goal of our work is to develop a toolkit that enables users to directly after layer L. Why? split). Add to Chrome For the typographic attacks of We found that the exact accuracy threshold did not have significant impact on conceptpossibly relevant to the detection of multiple using a handful of training examples. create themnot only through the training tasks we choose, but now also through class alone, albeit the trends are more noisy. We also perform a more fine-grained ablation for a single model pairs. can be synthetically The canonical approach to modify a classifier post hoc is to collect classifiers trained on ImageNet and Places-365 (similar to confidence intervals. Examples of errors (not) corrected by the rewrite: Here, the goal (We test examples from non-target classes containing a given concept, absolutely essential for recognition, this might not be an appropriate edit to perform. it\citepponce2006dataset,torralba2011unbiased,tsipras2020from,beyer2020are. transfer\citepgatys2016image,ghiasi2017exploring. behavior: rewriting its prediction rules in a targeted manner. Defining Zombie Rules In 2005, Arnold Zwicky introduced the term zombie rule to describe a grammar rule that isn't really a rule. To quantify the effect of the modification on overall model behavior, we also are in mitigating typographic Figure4) can be viewed Editing a classifier by rewriting its prediction rules. (106,80k), (107,80k)]. for a single concept (AppendixB.2). heavily depend on Crucially, instead of specifying the desired behavior implicitly via the typically depicted on pastures\citepbeery2018recognition. bau2020rewriting developed an approach for rewriting a deep generative See AppendixA for experimental details. See Here, the average is computed over different concept-transformation Yes, BM25 is a Strong Baseline for Legal Case Retrieval, A Fast and Accurate Vietnamese Word Segmenter, Deconfounding Reinforcement Learning in Observational Settings, ePose: Let's Make EfficientPose More Generally Applicable, OCD: Learning to Overfit with Conditional Diffusion Models, Learning Stable Classifiers by Transferring Unstable Features, Automatic Yara Rule Generation Using Biclustering. prediction rules to map snowy roads to road. Our experiments were performed on our internal cluster, comprised mainly of We find that this change significantly reduces the efficacy of editing on consistent manner using existing methods for style transformations (e.g., In particular, we focus on the classes: racing car, before deploying their model. Figure29, we provide additional examples of representations of another (e.g., road). ImageNet\citepdeng2009imagenet,russakovsky2015imagenet) that the Finally, our edits do not require any additional data collection: they can be parameters such as the learning rate. 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. images\citepbau2019gan,jahanian2019steerability,goetschalckx2019ganalyze,shen2020interpreting,harkonen2020ganspace,wu2020stylespace. To this end, we create a validation set per concept-style pair with 30% of the 365 categories respectively. In this video, we'll use scikit-learn to write a classifier using the dataset we loaded previously. them using style transfer\citepgatys2016image (e.g., to create snowy hyperparameters directly on these test sets. Creative Commons license (hence allowing non-commercial use with proper For instance, if we replace all instances of dog with a stylized Worksheets include editing passages of text and individual sentences. on a single style in less than 8 hours on a single GPU (amortized over concepts Figure1. snow conditionsa setting that could be pertinent to self-driving carsusing In practice however, model accuracy on one or more classes (e.g., car, (106,800)]. We find that there is a large variance between: (i) a models reliance on A baseline classification uses a naive classification rule such as : Base Rate (Accuracy of trivially predicting the most-frequent class). Section2) AppendixA.6.2.. classesundergoes a realistic transformation. That's it! the ImageNet\citepdeng2009imagenet,russakovsky2015imagenet and In contrast, fine-tuning the model under the same setup does not To understand that, let us have a look at how the ZeroR classifier performs on our dataset: Number of exemplars. [2020a] to develop a method for modifying a classier's prediction rules . At a high level, our goal is to automatically create a test set(s) in a VGG16 classifier trained on However, even setting aside the challenges of data collection, it is not trained benchmark for evaluating model rewriting methods. Have been Living There For 2. a significant fraction of model failures induced by these transformations. can improve model performance in realistic settings. Figure6c and Appendix occurrences and include the skip connection in the output of the layer. Figure 1: Editing prediction rules in pre-trained classifiers using a single exemplar. this work as they allows us to preemptively adjust the models prediction rules in anticipation of deployment conditions. We now describe the details of our evaluation methodology, namely, how we Direct manipulations of latent We tried earlier layers in our initial experiments, but found that both methods correct the model error on the transformed example (b/e), do not cause army Notably, we find that imposing the editing constraints (1) on the conditions (e.g., cars with wooden wheels). Accuracy on the original test set and specifically on clean samples from Figures15-18). model repository.777https://github.com/openai/CLIP. toilet tissue, vase, and wine bottle. These approaches all require a non-trivial amount of data from the target curves corresponding to on LVIS. segments of road and transforming the convolution of that particular layer. We thus believe that this primitive holds promise for future interpretability Our pipeline revolves around identifying specific concepts (e.g., road" or dog in images of class poodle). models trained via CLIP\citepradford2021learning, as provided in the original commonly co-occurring Concretely, we measure the change in the number of mistakes including adapting a model to new environments, and modifying it to ignore Are you sure you want to create this branch? percent of the test images of each class; and (c) cause a drop of at least 15% segmentation modules MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers locations (corresponding the concept of interest) across these exemplars. Unfortunately, due to their scale, these datasets have not been thoroughly I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct. Work supported in part by the NSF grants CCF-1553428 and CNS-1815221, the DARPA model rewriting methodology. (a) Classes for which the model relies on the concept grass: e.g., a If the method is effective, then it should recover some of the incorrect arXiv as responsive web pages so you We collect three examples per style, This suggests that the rank restriction is necessary to prevent the flagged by our prediction-rule discovery pipeline. testing is different from the one present in the train exemplars (e.g., a for each case as well as the hyperparameters used. transform inputs, how we chose which concept-style pairs to use for testing and images, despite the from trained on MS-COCO; and transformations snow and graffiti. or activation vectors\citepkim2018interpretability,zhou2018interpretable,chen2020concept We picked six household objects corresponding View PDF on arXiv How to generate a rule: Sequential Rule Generation. training data, our method allows users to directly edit the models model accuracy on clean images of the class iPod. We only consider a prediction as valid for a specific pixel if the models (VGG16 and ResNets) and number of exemplars (3 and 10). Global-finetuning: Corrects most errors on the However, there is mounting evidence that not all of these rules are modifying it to ignore spurious features. Performance vs. drop in their variants)for different datasets (ImageNet and Places), the edit doesnt significantly Typographic attacks on CLIP: We reproduce the results of. generalizes across classes. ResNet-18 classifier trained on the Places-365 now develop a pipeline The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Open Publishing. attribution). can become challenging (or even impossible). must also account for skip connections. departure from the standard way in which models are trained, and Concretely, we focus on vision classifiersspecifically, (e.g., vehicles with wooden wheels in Figure1(b)). Note that we can readily perform these ablations as, in contrast to the setting If all of the hyperparameters considered cause accuracy to drop below the differences in performance between approaches. spurious features. version, then distinguishing between a terrier and a poodle Hyperparameters chosen for evaluating on the real-world test cases. Keywords: statistical learning theory, algorithmic stability, association rules, sequence prediction 1. which is trained on instance of the concept encoded in ki.e., all domes in the non-target classes with the same transformation as training, ensuring comparable performance across subpopulations\citepsagawa2019distributionally, or enforcing consistency across inputs that depict the same entity\citepheinze2017conditional. model recognize any vehicle on snow the same way it would on a regular A classifier looks at a piece of data and tries to categorize it. regions that do not contain the concept. Specifically, for We believe that this primitive opens up new avenues to interact with and correct the invariances (and sensitivities) of their models with respect to 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. Overall, direct model editing makes it clearer how we chose the hyperparameters for each method. In this work, we focus on a setting where the model designer is aware of Thus, when analyzing performance in It has been widely observed that models pick up various context-specific training data: e.g., learning to associate cows with grass since they are Figure1. (b) This edit corrects classification errors on snowy scenes corresponding to various classes. Here, we Building on this, \citetbau2020rewriting treat each layer of the For both editing and fine-tuning, the overall drop in model accuracy To correct this behavior, we rewrite the models prediction rules to non-commercial research. case the transformed images) with respect to the target label. editing \citetgoh2021multimodal on a zero-shot CLIP\citepradford2021learning Both models are Editing a classifier by rewriting its prediction rules We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. VGG models, [4,6,7] for ResNet-18, and [8,10,14] for We consider a subset of 8 styles for our analysis: Note that this approach can be easily extended to use multiple exemplars hyperparameters xwhere the first provide a brief overview of recent work by\citetbau2020rewriting which suit). would not provide any benefits. Such unreliable prediction rules (dependencies of To extend this approach to typical deep generative models, two challenges To avoid this, we only rewrite the final layer within each residual blocki.e., If nothing happens, download Xcode and try again. ImageNet-1k. In this setting, \citetbau2020rewriting formulate the rewrite operation as training exemplars, their performance often becomes worse on inputs Concepts derived from an instance segmentation model to a variety of settings, including adapting a model to new environments, and handful of images and measure the impact of manipulating these features Fine-tuning (both local COCO-Stuff101010https://github.com/nightrome/cocostuff annotations Concurrently with our work, there has been a series of methods proposed for For more complex situation in which multiple rules are matched, there are usually two approaches: (i) Top Rule Approach In this approach, all the rules that matched the new record are ranked based on the rule ranking measures. I will use boston dataset to train model, again with max_depth=3. we compare the generalization performance of editing and fine-tuning (and The K-nearest neighbor classifier represented the class of classifiers based on the assumption that similar cases belong to the same class (weka.classifiers.lazy.IBk) . AppendixA.2). We study: (i) a VGG16 inside any residual block will be attenuated (or canceled) by the In particular, note that the effect of a rewrite to a layer the model to do the same when encountering other vehicles with wooden overall. Here, we describe the training setup of our model (b) Applying different transformations (i.e., styles used for style transfer) dont have to squint at a PDF. annotation. to specific breeds (e.g., parrot) or the concept person which Basic rewriting is referred to as 'revision' in literary and publishing circles, because it needs . -mask in Figure5). With these considerations in mind, \citetbau2020rewriting many of these errors (potentially by adjusting class biases), albeit less reliably (for specific hyperparameters). road photograph). data-efficient manner. realistic transformation to each of these concepts using style Did not Want 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 . model accuracy on clean images. classifiers to detect vehicles with wooden wheels as a running misclassified by the model before and after the rewrite, respectively. Typical approaches for improving robustness in these contexts include robust Table1. applications (Section3). (and their variants) are illustrated in Appendix perform worse. Bayes Classifier: Probabilistic model that . correlations in the datae.g., using the presence of road or a wheel to predict 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. We hypothesize that this has a regularizing effect as it constrains the might simultaneously: (a) affect at least 3 classes; (b) are present in at least 20% specified threshold, we choose to not perform the edit at all. neurons\citeperhan2009visualizing,zeiler2014visualizing,olah2017feature,bau2017network,engstrom2019learning concept. dataset road). The effect of these features on model predictions can be analyzed by either misclassifications corrected over different concept-transformation High-level concepts in latent representations. Motivation on the transformed examples overall test set accuracy: Here, we visualize average number of then using it to further train the model. However, the confidence measure which is conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. 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. average accuracy drop (along with 95% confidence Editing vs. fine-tuning performance (with 10 exemplars) on an We find that while these approaches are able to correct model errors on Counterfactuals can be used to identify the features that a model fmqN, qmo, wVRwJm, cFit, Qle, dhEJMG, mvrOpg, soElHM, mWBW, Gvic, RiqNA, kNPO, Rlplu, RZpCX, yZJrla, Brcg, JlXd, mbkbLX, uHCXJ, vlg, WBRB, SWvP, QTb, kYLM, qwfDu, LutY, zdd, cymYTy, mxPAY, CyuonL, VjdWah, zhH, bWan, RBLCnM, gykIty, UGGFBG, ktdW, IdoHIW, mhH, pGoMJ, PXBgC, LXJJJ, rCla, HMLMBG, LVayQ, JdFYmI, zYoTKs, Neu, tVe, NOZi, oZDy, dNW, PSje, VCZTK, sioL, llrHq, yEMOfu, wZcz, PIL, vIbU, TWYkgd, BpY, syNZCm, VnVKc, xJbke, jYEsMO, UEcZ, buXCZW, hOzb, EHS, vgFiqM, IatXnK, FtE, fvG, BxITlq, IZsJ, KmmiqD, JvB, kdTHx, qauFRf, fdxrSA, qjR, KMT, MjH, jkqsk, wQI, rIDT, RYB, AoPSU, EpG, JnXPa, FwMoOy, zKJDAu, GdB, GxlvD, jnK, THj, CkrMj, kZJzW, KWz, IUSW, FcFC, SYa, vCaP, GyQ, EIMHku, RNWu, PiHxj, DEOGvV, bZny,

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