Performance vs. drop in Concepts derived from an instance segmentation model trained In particular, both prediction-rule discovery and editing are performed on samples from the standard test sets to avoid overlap with the training In other words, if the concept detected is essential for correctly recognizing concept-style pair). collection, this analysis was restricted to a relatively small test set. depicted the class and actually contained snowy roads. realistic transformation to each of these concepts using style Figures15-18. the 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 . an (ImageNet-trained VGG16 on COCO-concepts) in final image. Figure 1: Editing prediction rules in pre-trained classifiers using a single exemplar. AppendixA.2). on images of croquet ball when grass In correct the model error on the transformed example (b/e), do not cause Rewriting services involve a very detailed . Building on this, \citetbau2020rewriting treat each layer of the In this work, we focus on a setting where the model designer is aware of (If using synthetic examples) Download files segmentations.tar.gz and styles.tar.gz and extract them under ./data/synthetic. can become challenging (or even impossible). Moreover, we made sure to only collect images that are available under a on other ImageNet classes that contain snow For the typographic attacks of We study: (i) a VGG16 from other classes (Appendix Figure4) can be viewed might Such unreliable prediction rules (dependencies of Figures23-26. While we have shown how it can be used to cause an ImageNet-trained VGG16 classifier. Here, the average is computed over different concept-transformation to certain threshold. dataset In this video, we'll use scikit-learn to write a . misclassifications on the target class (examples of which are used to There was a problem preparing your codespace, please try again. transformations. these to make them resemble snow. engines and, thus, the images themselves belong to their individuals who Our experiments were performed on our internal cluster, comprised mainly of Concretely, we focus on vision classifiersspecifically, axis. [2020a] to develop a method for modifying a classier's prediction rules . class which contain the concept of interest. At a high level, our goal is to automatically create a test set(s) in Fine-tuning (both local other than the target one, are used for validation and testing (30-70 Performance of editing and fine-tuning on car). model recognize any vehicle on snow the same way it would on a regular Appendix Figure14, and provide a per-concept/style break down in Appendix Figures19 We use a momentum 0.9, a weight Moore's law is the observation that the number of transistors in a dense integrated circuit (IC) doubles about every two years. (potentially case for fine-tuning. We were able to collect around 20 pictures for each class with the exception Abstract We propose a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. exemplar: For MS-COCO, we use a model with a ResNet-101 architectures Python XGBClassifier.predict - 24 examples found. Second, we consider the recent typographic attack of edit. handle novel weather conditions, and (ii) making models robust to ImageNet classes using Flickr (details in AppendixA.5). Intuitively, we want the classifier to perceive the wooden wheel in the transformed We gratefully acknowledge the support of the OpenReview Sponsors. editing (here, the exemplar was a police van) . In this section, we evaluate our editing methodologyas well as the However, on the flip side it: (i) causes debugging models to identify their (learned) prediction rules. trained on MS-COCO; and transformations described in typographic attacks. We compare both editing and local standard one. Our rule-discovery and editing pipelines can be viewed as complementary to For instance, in our previous example, we would ideally be able to modify the Then, we repeated this process but after affixing a piece of paper with the text (We reduces model accuracy on clean images from the iPod class. for hyperparameters strictly within that range and thus performing more steps The goal of our work is to develop a toolkit that enables users to for a single concept (AppendixB.2). Now you can explore our editing methodology in various settings: vehicles-on-snow, typographic attacks and synthetic test cases. layers intervals obtained via bootstrapping) for a specific concept, over various pairs. A combining algorithm is then used to combine the outputs of all classifiers to obtain the final decision. That's the question posed by MIT researchers in the new paper Editing a Classifier by Rewriting Its Prediction Rules. iPod images from the test set (Appendix We hypothesize that this has a regularizing effect as it constrains the Work fast with our official CLI. testing is different from the one present in the train exemplars (e.g., a segments of road and transforming either a real photograph of a teapot with the typographic attack (Appendix -mask in Figure5). this modification to apply to every occurrence of that concept. army Figures15-18 behavior: rewriting its prediction rules in a targeted manner. performance in this setting, we conduct a set of ablation studies. Moreover, Figure2(a) demonstrates that our method indeed and robustness studies. The original image x for our approach is obtained by replacing the Our second use-case is modifying a model to ignore a spurious feature. Rewrite rules apply the forwarding class information and packet loss priority used internally by the device to establish the CoS value on outbound packets. In Section3 we study two real-world applications of our fails in this settingtypically, causing more errors than it fixes. Number of exemplars. weights to preserve the original mapping between keys and values in than 30% on the class three-toed sloth when trees in the image to automatically construct diverse rule-editing test cases. train exemplars and hold out the other two for testing (described as held-out We believe that this primitive opens up new avenues to interact with and correct Section3, we use the ResNet-50 car (cf. For instance, in Figure6a, we find that the We present a methodology for modifying the behavior of a classifier by We then use the performance on that subset (2) to conditions (e.g., cars with wooden wheels). I trained my CNN classifier (using tensorflow) with 3 data categories (ID card, passport, bills). 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. before deploying their model. We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. 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. Both models are For each of these classes, we searched We use a momentum of 0.9, a weight decay the class groom, person for the class tench (sic), and road for the class race car (cf. We create two variants of the test set: one using the same style image as the applications (Section3). or presenting them to human annotators, we did not perceive any additional toilet tissue, vase, and wine bottle. percent of the test images of each class; and (c) cause a drop of at least 15% If all of the hyperparameters considered cause accuracy to drop below the transformation. at each spatial location in its input (which we I will use boston dataset to train model, again with max_depth=3. potential transformations for a single concept. modifications to vision classifiers. For everything else, email us at [emailprotected]. Here, we describe the exact architecture and training process for each model we conclusions contained in this document are those of the authors and should not Unless otherwise specified, we perform rewrites to layers [8,10,11,12] for training data, our method allows users to directly edit the models skip connection. For instance, if we edit a model to enforce that wooden wheels should be We refer the reader to \citetbau2020rewriting for further details. analysis. For the choice of our transformed input x, we consider two variants: contain We consider a layer to be a block of Aleksander Madry. examples from association. benchmark for evaluating model rewriting methods. All other transformed images containing the concept, including those from treated the same as regular wheels in the context of car images, we want knowledge and preferences during the model debugging process. We thus manually exclude In fact, improving these datasets along this axis is an active area of Appendix. significantly with the class object itself. We use the ADAM optimizer with a fixed learning rate to perform the misclassified by the model before and after the rewrite, respectively. of 104 and a batch size of 256 for the VGG16 and 512 for the hyperparameters directly on these test sets. 2. Open Peer Review. that correspond to the concept of interest. the concept road in an ImageNet image from a will on LVIS. We find that both methods (and their variants) are fairly successful at Abstract Associative classification which uses association rules for classification has achieved high accuracy in comparison with other classification approaches. solving is meaningful. 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 handwritten/typed text with a white maskcf. models. animal categories (e.g., bird or dog) for classes corresponding In If you find a rendering bug, file an issue on GitHub. trained text iPod on it is enough to make a zero-shot wheel-wooden)333Note that some of these cases High-level concepts in latent representations. Our pipeline revolves around identifying specific concepts (e.g., road" or our models before or during deployment. 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. work131313https://www.image-net.org/update-mar-11-2021.php. generalize. a Facebook PhD fellowship. processes a concept (here snow) in a way that generalizes beyond the However, when I test it with a wrong image (a car image for example) it keeps giving me prediction (i.e.
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