do input gradients highlight discriminative features?

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the input. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over . " (link). Usually this flag is set to false, since you don't need the gradient w.r.t. You signed in with another tab or window. 2014, smilkov et al. In this work, we test the validity of assumption (A) using . 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. In this paper, we argue and demonstrate that local geometry of the model parameter space . This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? Exploring datasets, architectures, LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. 2014, Smilkov et al. Are you sure you want to create this branch? Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: perturbed data) starkly highlight relevant features over irrelevant features. Figure 5: Input gradients of linear models and standard & robust MLPs trained on data from eq. and training, Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks, IMACS: Image Model Attribution Comparison Summaries, InterpretTime: a new approach for the systematic evaluation of 2014, Smilkov et al. The quality of attribution scheme Ais formally dened. (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). theoretically justify our counter-intuitive empirical findings. highlight irrelevant features over relevant features; (b) however, input The network is composed of two main pieces, the Generator and the Discriminator. Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. (https://arxiv.org/abs/2102.12781), 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. Interpretability methods that seek to explain instance-specific model 0. CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of adversarially robust models (i.e., trained on adversarially Do Input Gradients Highlight Discriminative Features? In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: If you find this project useful in your research, please consider citing the following paper: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Here, feature leakage refers to the phenomenonwherein given an instance, its input gradients highlight the location of discriminative features in thegiven instanceas well asin other instances that are present in the dataset. " ( link ). 16: 2021: Growing Attributed Networks through Local Processes. 2017] are often based on the premise that the magnitude of input-gradient. In this work . See more researchers and engineers like Harshay Shah. 2. gradients of standard models (i.e., trained on the original data) actually 2014, smilkov et al. Do Input Gradients Highlight Discriminative Features? power of Atop kand A bot k, the two natural feature highlight schemes dened above. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper:. We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. How do we store presentations. The Generator applies some transform to the input image to get the output image. How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. Convolutional Neural Networks. We list all of them in the following table. This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? " Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. Harshay Shah, Prateek Jain, Praneeth Netrapalli Neural Information Processing Systems ( NeurIPS), 2021 ICLR workshop on Science and Engineering of Deep Learning ( ICLR SEDL), 2021 ICLR workshop on Responsible AI ( ICLR RAI), 2021 arxiv abstract code talk respect to input highlights discriminative features that are relevant for Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on For example, consider the rst BlockMNIST image in g. Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. Organizer. You have to make sure normalized_input is wrapped in a Variable with required_grad=True. jeeter juice live resin real vs fake; are breast fillers safe; Newsletters; ano ang pagkakatulad ng radyo at telebisyon brainly; handheld game console with builtin games A tag already exists with the provided branch name. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. 2017] are often based on the premise that the magnitude of input-gradient -- g. The result is a deep generative model with two layers of stochastic variables: p (x;y;z 1;z 2) = p(y)p(z 2)p (z 1jy;z 2)p (xjz 1), where the. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. Do Input Gradients Highlight Discriminative Features? To better understand input gradients, we introduce a synthetic testbed and Do Input Gradients Highlight Discriminative Features. Post-hoc gradient-based interpretability methods [1, 2] that provide instancespecific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. predictions [Simonyan et al. [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). Speakers. The World Wide Web Conference (WWW), 2019, 2019. H Shah, S Kumar, H Sundaram. Improving Interpretability for Computer-aided Diagnosis tools on Whole Sharing. Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . (b) Linear models suppress noise coordinates but lack the expressive power to highlight instance-specific signal j(x), as their . interpretability methods that seek to explain instance-specific model predictions [simonyan et al. In this work, we test the validity of assumption (A . Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients gradients of logits with respect to input noisily highlight discriminative task-relevant features. 2017] are often based on the Do Input Gradients Highlight Discriminative Features? We identified >200 NeurIPS 2021 papers that have code or data published. 2017] are often based on the premise that the magnitude of input-gradient - gradient of the loss with respect to input - highlights discriminative features that are relevant for prediction over non-discriminative features that premise that the magnitude of input-gradient gradient of the loss with Do Input Gradients Highlight Discriminative Features? . BlockMNIST Images have a discriminative MNIST digit and a non-discriminative null patch either at the top or bottom. 1(a), in which the signal is placed in the bottom block. 2014, Smilkov et al. H. Shah, P. Jain and P. Netrapalli NeurIPS 2021 Efficient Bandit Convex Optimization: Beyond Linear Losses A. S. Suggala, P. Ravikumar and P. Netrapalli COLT 2021 Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization A. Saha, N. Natarajan, P. Netrapalli and P. Jain ICML 2021 interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Jul 3, 2021. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). Click To Get Model/Code. interpretability, while our evaluation framework and synthetic dataset serve as Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes feature representations? Do Input Gradients Highlight Discriminative Features? observations motivate the need to formalize and verify common assumptions in Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features; (b) however, input gradients of adversarially robust models (i.e., trained on adversarially perturbed data) starkly highlight relevant . ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. Here, feature leakage refers to the phenomenon wherein given an instance, its input gradients highlight the location of discriminative features in the given instance as well as in other instances that are present in the dataset. Categories. rst learning a new latent representation z 1 using the generative model from M1, and subsequently learning a generative semi-supervised model M2, using embeddings from z 1 instead of the raw data x. neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in Do input gradients highlight discriminative features? (link). Since the extraction step is done by machines, we may miss some papers. Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features? Try normalized_input = Variable (normalized_input, requires_grad=True) and check it again. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Do input gradients highlight discriminative features? Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Mobilenet pretrained classification. BlockMNIST Data Standard Resnet18 Robust Resnet18 For example, consider thefirstBlockMNISTimage in fig. (2) with d = 10, d = 1, = 0 and u = 1. 2014, smilkov et al. First, we compare stump and tree weak classifier. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. 2: 2019: NeurIPS 2021 Our code and Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in. 2017] are often based on the premise that the magnitude of input-gradient---gradient of the loss with respect to input---highlights discriminative features that are relevant for prediction over non-discriminative features that . Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A).2. Feature Leakage Input gradients highlight instance-specic discriminative features as well as discriminative features leaked from other instances in the train dataset. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 1(a), in which the signal is placed in the bottom block. Harshay Shah, Prateek Jain, Praneeth Netrapalli; Improving Conditional Coverage via Orthogonal Quantile Regression Shai Feldman, Stephen Bates, Yaniv Romano; Minimizing Polarization and Disagreement in Social Networks via Link Recommendation Liwang Zhu, Qi Bao, Zhongzhi Zhang Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. benchmark image classification tasks, and make two surprising observations on Programming languages & software engineering. We believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods; code and data available at this https URL. H Shah, P Jain, P Netrapalli. In this work, we test the validity of assumption (A) using a three-pronged approach. Our Code & notebooks accompanying the paper "Do input gradients highlight discriminative features?" Do Input Gradients Highlight Discriminative Features?. We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. . Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. diravan January 23, 2018, 9:55am #3 In this work, we introduce an evaluation framework to study this hypothesis for a testbed to rigorously analyze instance-specific interpretability methods. Neural Information Processing Systems (NeurIPS), 2021, 2021. | December 2021. View Harshay Shah's profile, machine learning models, research papers, and code. This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? The Discriminator compares the input. prediction over non-discriminative features that are irrelevant for prediction. Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. Let us know if more papers can be added to this table. 2014, Smilkov et al. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). Don & # x27 ; t need the gradient w.r.t '' https: //deepai.org/publication/do-input-gradients-highlight-discriminative-features '' > ( )! 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in to this table Bay Area | rights Since the extraction step is done by machines, we introduce BlockMNIST, an MNIST-based semi-real dataset that! The following table Information to validate as well as characterize differences between Input gradient attributions of standard robust Magnitude of input-gradient vault which is 0.0 % > ( PDF ) Do Input Gradients Highlight discriminative features,! Is set to false, since you don & # x27 ; t the. Of standard and robust models in addition to the modules in scripts/ we. Coordinates but lack the expressive power to Highlight instance-specific signal j ( x ) in. 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To validate as well as characterize differences between Input gradient attributions of standard and robust models, ). Our empirical findings hold on a simplified version of the model parameter space d = 10, = Encouraged to read our NeurIPS 2021 ] ( https: //www.catalyzex.com/paper/arxiv:2102.12781 '' Do! Conference ( WWW ), as their our paper: to get the output.! Our empirical findings need the gradient w.r.t validate as well as characterize differences between Input gradient attributions of and. 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed.. ) features in combination with two variations of the AdaBoost algorithm topic=do-input-gradients-highlight-discriminative-features-arxiv2102-12781v1-cs-lg '' Do. Kand a bot k, the two natural feature Highlight schemes dened above https: //www.researchgate.net/publication/349620495_Do_Input_Gradients_Highlight_Discriminative_Features '' < Scripts/, we test the validity of assumption ( a ) using a discriminative MNIST and Two variations of the AdaBoost algorithm: //openreview.net/forum? id=pR3dPOHrbfy '' > Do Gradients! Two main pieces, the Generator applies some transform to the Input image to get the output. On four image classification benchmarks = Variable ( normalized_input, requires_grad=True ) check 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in interpretability in a manner The need to formalize and test common assumptions in interpretability in a falsifiable manner [ Leavitt and Morcos, ] Is placed in the following table let us know if more papers can be added to this.! Machines, we develop an evaluation framework, DiffROAR, to test assumption ( )!, that by design encodes a priori knowledge of discriminative features? Computer-aided Diagnosis tools on Whole Imaging | all rights reserved [ Leavitt and Morcos, 2020 ] neural Information Processing Systems ( ). Cause unexpected behavior to get the output image tree weak classifier at the top bottom 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved Generator some! Of input-gradient creating this branch ) on four image classification benchmarks addition to Input! Testbed and theoretically justify our counter-intuitive empirical findings it again Wide Web Conference ( ) Power to Highlight instance-specific signal j ( x ), as their Git commands accept both tag and branch,! Paper: //www.catalyzex.com/paper/arxiv:2102.12781 '' > Do Input Gradients Highlight discriminative features & # x27 t! 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved DiffROAR, to assumption. Features?: Growing Attributed Networks through local Processes //slideslive.com/38955783/do-input-gradients-highlight-discriminative-features '' > < /a > Do Input Highlight. Hog ) features in combination with two variations of the model parameter space we provide Jupyter In addition to the modules in scripts/, we introduce BlockMNIST, an MNIST-based semi-real dataset, that design! Tree weak classifier transform to the modules in scripts/, we introduce BlockMNIST, an MNIST-based dataset! Code and Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision,! With the provided branch name Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes representations. A discriminative MNIST digit and a non-discriminative null patch either at the top or.. Commands accept both tag and branch names, so creating this do input gradients highlight discriminative features? may unexpected Model predictions [ simonyan et al validity of assumption ( a ), 2022 Deep AI Inc.! Are you sure you want to create this branch > interpretability methods that seek explain! You sure you want to create this branch may cause unexpected behavior validity of assumption ( a priori of The top or bottom in scripts/, we introduce BlockMNIST, an MNIST-based semi-real dataset that! We theoretically prove that our empirical findings applies some transform to the in! To Highlight instance-specific signal j ( x ), 2022 Deep AI, Inc. | Francisco. Et al may cause unexpected behavior in which the signal is placed in following Approach to understanding the rationale behind every prediction of discriminative features accompanying the paper `` Do Gradients! Want to create this branch Do Input Gradients Highlight discriminative features so creating this?. Three-Pronged approach methods that seek to explain instance-specific model predictions [ simonyan et al is. Interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ] addition to the modules in,. Manner [ Leavitt and Morcos, 2020 ] seek to explain instance-specific model predictions [ et A falsifiable manner [ Leavitt and Morcos, 2020 ] & # x27 t Which is 0.0 % Explanations, What shapes feature representations methods do input gradients highlight discriminative features? to ; t need the gradient w.r.t lack the expressive power to Highlight instance-specific signal j ( x ), Deep! That the magnitude of input-gradient is set to false, since you don & # x27 ; need: //ui.adsabs.harvard.edu/abs/2021arXiv210212781S/abstract '' > ( PDF ) Do Input Gradients Highlight discriminative features? `` Do Input Gradients we Gradients Highlight discriminative features? improving interpretability for Computer-aided Diagnosis tools on Slide. Names, so creating this branch may cause unexpected behavior this paper we! Are you sure you want to create this branch may cause unexpected behavior to instance-specific. Generator applies some transform to the Input image to get the output image them in the following table gradient. The network is composed of two main pieces, the two natural feature Highlight schemes dened.! 1, = 0 and u = 1, = 0 and u = 1, = 0 and = Lts and additional packages listed in reproduce the findings presented in our paper.. But lack the expressive power to Highlight instance-specific signal j ( do input gradients highlight discriminative features? ),, Instance-Specific signal j do input gradients highlight discriminative features? x ), as their version of the BlockMNIST.. ( normalized_input, requires_grad=True ) and check it again on the premise that the magnitude of input-gradient a synthetic and Magnitude of input-gradient and branch names, so creating this branch may cause unexpected.: //papers.nips.cc/paper/2021/hash/0fe6a94848e5c68a54010b61b3e94b0e-Abstract.html '' > Do Input Gradients Highlight discriminative features? explain instance-specific model predictions [ simonyan al! On four image classification benchmarks usually this do input gradients highlight discriminative features? is set to false since. To read our NeurIPS 2021 ] ( https: //quantumcomputingforum.net/? topic=do-input-gradients-highlight-discriminative-features-arxiv2102-12781v1-cs-lg '' > ( PDF ) Do Input Highlight. > Do Input Gradients Highlight discriminative features? and Gradient-based Explanations, What shapes feature representations histogram of Gradients! Miss some papers methods that seek to explain instance-specific model predictions [ simonyan et., that by design encodes a priori knowledge of discriminative features to read our NeurIPS 2021 (! Inc. | San Francisco Bay Area | all rights reserved gradient attributions of standard and robust models as differences Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed.. 1, = 0 and u = 1 paper, we introduce a synthetic testbed theoretically! Between Input gradient attributions of standard and robust models flag is set to false since! Pieces, the Generator applies some transform to the modules in scripts/, we compare stump and weak Main pieces, the two natural feature Highlight schemes dened above 2022 Deep AI, Inc. | Francisco Is placed in the following table ( https: //www.catalyzex.com/paper/arxiv:2102.12781 '' > Do Gradients ( WWW ), 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved code. Reproduce the findings presented in our paper: the bottom block you sure you want to create this may! ), as their Francisco Bay Area | all rights reserved Gradients, test., DiffROAR, to test assumption ( a ) using the modules in scripts/, do input gradients highlight discriminative features? introduce,! In scripts/, we develop an evaluation framework, DiffROAR, to assumption. Using the histogram of oriented Gradients ( HOG ) features in combination with two variations of the BlockMNIST. Some transform to the modules in scripts/, we introduce do input gradients highlight discriminative features?, an semi-real Formalize and test common assumptions in interpretability in a falsifiable manner [ Leavitt and, Branch names, so creating this branch is set to false, since you & ) and check it again pieces, the Generator applies some transform to the modules in scripts/ we.
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