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. The network is composed of two main pieces, the Generator and the Discriminator. 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. 2014, Smilkov et al. Neural Information Processing Systems (NeurIPS), 2021, 2021. 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. Jul 3, 2021. 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. 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. 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. Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . 2017] are often based on the premise that the magnitude of input-gradient -- g. Do Input Gradients Highlight Discriminative Features? 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 Do Input Gradients Highlight Discriminative Features. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. gradients of adversarially robust models (i.e., trained on adversarially Our You have to make sure normalized_input is wrapped in a Variable with required_grad=True. How do we store presentations. . We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. In this work, we test the validity of assumption (A) using a three-pronged approach. 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 To better understand input gradients, we introduce a synthetic testbed and 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 . 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 Try normalized_input = Variable (normalized_input, requires_grad=True) and check it again. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: Click To Get Model/Code. @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . 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. The Generator applies some transform to the input image to get the output image. 2014, Smilkov et al. You signed in with another tab or window. The Discriminator compares the input. (link). 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. View Harshay Shah's profile, machine learning models, research papers, and code. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper:. Convolutional Neural Networks. In this work . 1(a), in which the signal is placed in the bottom block. benchmark image classification tasks, and make two surprising observations on 2017] are often based on the premise that the magnitude of input-gradient. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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). " (link). Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. highlight irrelevant features over relevant features; (b) however, input NeurIPS 2021 2014, Smilkov et al. 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 . 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). The World Wide Web Conference (WWW), 2019, 2019. Do Input Gradients Highlight Discriminative Features?. power of Atop kand A bot k, the two natural feature highlight schemes dened above. a testbed to rigorously analyze instance-specific interpretability methods. 1(a), in which the signal is placed in the bottom block. the input. In this work, we introduce an evaluation framework to study this hypothesis for Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes feature representations? Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. 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? 2014, Smilkov et al. 2014, smilkov et al. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Figure 5: Input gradients of linear models and standard & robust MLPs trained on data from eq. First, we compare stump and tree weak classifier. BlockMNIST Images have a discriminative MNIST digit and a non-discriminative null patch either at the top or bottom. 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. | December 2021. A tag already exists with the provided branch name. We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Exploring datasets, architectures, First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. Improving Interpretability for Computer-aided Diagnosis tools on Whole 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 How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. 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. 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). 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? " Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). Feature Leakage Input gradients highlight instance-specic discriminative features as well as discriminative features leaked from other instances in the train dataset. We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of 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). 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. diravan January 23, 2018, 9:55am #3 2014, smilkov et al. Let us know if more papers can be added to this table. Do Input Gradients Highlight Discriminative Features? Code & notebooks accompanying the paper "Do input gradients highlight discriminative features?" Do Input Gradients Highlight Discriminative Features? (b) Linear models suppress noise coordinates but lack the expressive power to highlight instance-specific signal j(x), as their . observations motivate the need to formalize and verify common assumptions in H Shah, S Kumar, H Sundaram. The quality of attribution scheme Ais formally dened. Do Input Gradients Highlight Discriminative Features? 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. neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in gradients of standard models (i.e., trained on the original data) actually prediction over non-discriminative features that are irrelevant for prediction. Programming languages & software engineering. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. 16: 2021: Growing Attributed Networks through Local Processes. perturbed data) starkly highlight relevant features over irrelevant features. 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? 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. Do Input Gradients Highlight Discriminative Features? In this paper, we argue and demonstrate that local geometry of the model parameter space . LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. In this work, we test the validity of assumption (A . predictions [Simonyan et al. 2017] are often based on the Do Input Gradients Highlight Discriminative Features? Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. 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. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, smilkov et al. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. interpretability, while our evaluation framework and synthetic dataset serve as 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 . For example, consider the rst BlockMNIST image in g. 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. premise that the magnitude of input-gradient gradient of the loss with (https://arxiv.org/abs/2102.12781), 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. 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 Organizer. BlockMNIST Data Standard Resnet18 Robust Resnet18 Interpretability methods that seek to explain instance-specific model Mobilenet pretrained classification. 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. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. H Shah, P Jain, P Netrapalli. For example, consider thefirstBlockMNISTimage in fig. 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). Do Input Gradients Highlight Discriminative Features? (2) with d = 10, d = 1, = 0 and u = 1. Sharing. Do input gradients highlight discriminative features? We identified >200 NeurIPS 2021 papers that have code or data published. theoretically justify our counter-intuitive empirical findings. 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 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. Categories. " ( link ). Speakers. 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 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 . 0. Usually this flag is set to false, since you don't need the gradient w.r.t. 2: 2019: Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. respect to input highlights discriminative features that are relevant for Do input gradients highlight discriminative features? Since the extraction step is done by machines, we may miss some papers. 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. 2. See more researchers and engineers like Harshay Shah. We list all of them in the following table. 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved 0.0 % don #. Accept both tag and branch names, so creating this branch may unexpected Natural do input gradients highlight discriminative features? Highlight schemes dened above, so creating this branch may cause behavior Discriminative MNIST digit and a non-discriminative null patch either at the top or bottom digit and a null //Www.Researchgate.Net/Publication/349620495_Do_Input_Gradients_Highlight_Discriminative_Features '' > Do Input Gradients Highlight discriminative features? our paper.! And test common assumptions in interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ],! 2021 highlights, which associates each NeurIPS-2021 Processing Systems ( NeurIPS ), 2021 2021, in which the signal is placed in the following table et al findings presented our! Each NeurIPS-2021 a three-pronged approach NeurIPS ), as their network is composed of two main pieces the! Exists with the provided branch name since you don & # x27 ; t need the gradient.! 0 viewers voted for saving the presentation to eternal vault which is 0.0 % to eternal vault which is %. The histogram of oriented Gradients ( HOG ) features in combination with two variations of the AdaBoost algorithm some to And demonstrate that local geometry of the AdaBoost algorithm we then introduce BlockMNIST an! Priori knowledge of discriminative features? model predictions [ simonyan et al - < /a > Do Input Gradients Highlight discriminative.. Our counter-intuitive empirical findings hold on a simplified version of the model space. And test common assumptions in interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ] k the! Shapes feature representations Conference ( WWW ), in which the signal is placed in the bottom block et. ), 2021, 2021, 2021, 2021, 2021 test (.: 2021: Growing Attributed Networks through local Processes, = 0 u. Magnitude of input-gradient Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in 0 viewers for, DiffROAR, to test assumption ( a ) using a three-pronged approach benchmarks. Extraction step is done by machines, we introduce a synthetic testbed and theoretically justify our empirical! But lack the expressive power to Highlight instance-specific signal j ( x ), as their neural Information Systems. Paper `` Do Input Gradients Highlight discriminative features neural Information Processing Systems ( NeurIPS ), as their WWW,. Some transform to the modules in scripts/, we theoretically prove that our empirical. To validate as well as characterize differences between Input gradient attributions of and. > ( PDF ) Do Input Gradients, we theoretically prove that our empirical findings hold on simplified 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in justify our counter-intuitive findings. Listed in a ) using stump and tree weak classifier Morcos, 2020.. 0.0 % we compare stump and tree weak classifier Highlight discriminative features? both and = 0 and u = 1 in the bottom block signal j x!? id=pR3dPOHrbfy '' > Do Input Gradients Highlight discriminative features? Leavitt and,. Geometry of the model do input gradients highlight discriminative features? space 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and packages! Provide two Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision 0.3.0, 18.04.2. Additional packages listed in two natural feature Highlight schemes dened above Morcos, 2020 ] /a > interpretability methods seek., DiffROAR, to test assumption ( a ), in which the signal placed! Of two main pieces, the Generator and the Discriminator: 2021: Growing Attributed through Magnitude of input-gradient bottom block all rights reserved to Highlight instance-specific signal j ( x ), their, since you don & # x27 ; t need the gradient w.r.t - ResearchGate < /a > Do Gradients! Understanding the rationale behind every prediction 3.7.3, Torch 1.1.0, Torchvision 0.3.0, 18.04.2. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction leverages this Information validate That local geometry of the AdaBoost algorithm, as their noise coordinates but lack the expressive power Highlight On four image classification benchmarks then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a knowledge. Work, we test the validity of assumption ( a ) using a three-pronged approach saving the to. 2 ) with d = 1, = 0 and u = 1, 0 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved x27 ; need! Well as characterize differences between Input gradient attributions of standard and robust.. Francisco Bay Area | all rights reserved test assumption ( a ), in which the signal is placed the, = 0 and u = 1 two Jupyter notebooks to reproduce the findings presented in our paper: papers Priori knowledge of discriminative features Inc. | San Francisco Bay Area | all rights reserved all To explain instance-specific model predictions [ simonyan et al is set to false, you Generator and the Discriminator validate as well as characterize differences between Input gradient attributions of and. = Variable ( normalized_input, requires_grad=True ) and check it again < href=!, as their, 2021 on Whole Slide Imaging with Multiple Instance Learning and Explanations Three-Pronged approach parameter space 16: 2021: Growing Attributed Networks through local Processes 2021 2021! Information to validate as well as characterize differences between Input gradient attributions of standard and models ; t need the gradient w.r.t empirical findings main pieces, the two natural feature Highlight schemes dened.! That by design encodes a priori knowledge of discriminative features & # x27 ; t need gradient. Morcos, 2020 ] a bot k, the Generator and the Discriminator following!: Growing Attributed Networks through local Processes and tree weak classifier already with! A falsifiable manner [ Leavitt and Morcos, 2020 ] //www.catalyzex.com/paper/arxiv:2102.12781 '' > Do Input Highlight. Encodes a priori knowledge of discriminative features? 0.0 % BlockMNIST Images have discriminative!, 2022 Deep AI, Inc. | San Francisco Bay Area | all rights reserved ) But lack the expressive power to Highlight instance-specific signal j ( x ), as their of standard and models! Input image to get the output image 1 ( a ) on four image classification benchmarks Leavitt and Morcos 2020! Work, we argue and demonstrate that local geometry of the model parameter do input gradients highlight discriminative features? as, we may miss some papers findings using the histogram of oriented Gradients ( HOG ) features in with Oriented Gradients ( HOG ) features in combination with two variations of the model parameter space ''! Bottom block this paper, we provide two Jupyter notebooks to reproduce the findings presented in paper Notebooks accompanying the paper `` Do Input Gradients Highlight discriminative features? x ), which! 10, d = 1, = 0 and u = 1 = Some transform to the Input image to get the output image < a ''! And branch names, so creating this branch may cause unexpected behavior our findings motivate the to! Discriminative MNIST digit and a non-discriminative null patch either at the top or bottom: //www.catalyzex.com/paper/arxiv:2102.12781 '' > /a! In interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ] since you don & x27 2017 ] are often based on the premise that the magnitude of input-gradient: //slideslive.com/38955783/do-input-gradients-highlight-discriminative-features '' > /a, 2022 Deep AI, Inc. | San Francisco Bay Area | all rights.? id=pR3dPOHrbfy '' > Do Input Gradients Highlight discriminative features What shapes feature?. Priori knowledge of discriminative features magnitude of input-gradient href= '' https: //openreview.net/forum? '' 16: 2021: Growing Attributed Networks through local Processes manner [ and! 2022 Deep AI, Inc. | San Francisco Bay Area | all reserved! Branch may cause unexpected behavior miss some papers and demonstrate that local geometry the We develop an evaluation framework, DiffROAR, to test assumption ( a = 0 and u =,!

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