On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations | Paper Citation Record

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This paper cites Sanity Checks for Saliency Maps.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Sanity Checks for Saliency Maps

Reference 1

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Reference 2

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This paper cites On Pixel-Wise Explanations for Non-Linear Classi- fier Decisions by Layer-Wise Relevance Propagation.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On Pixel-Wise Explanations for Non-Linear Classi- fier Decisions by Layer-Wise Relevance Propagation

Reference 3

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This paper cites The Shattered Gradients Problem: If resnets are the answer, then what is the question?.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations The Shattered Gradients Problem: If resnets are the answer, then what is the question?

Reference 4

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This paper cites Feature learning as alignment: a structural property of gradient descent in non-linear neural networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Feature learning as alignment: a structural property of gradient descent in non-linear neural networks

Reference 5

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Reference 6

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This paper cites Deep Equals Shallow for ReLU Networks in Kernel Regimes.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Deep Equals Shallow for ReLU Networks in Kernel Regimes

Reference 7

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This paper cites On the Inductive Bias of Neural Tangent Kernels.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On the Inductive Bias of Neural Tangent Kernels

Reference 8

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This paper cites Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Reference 9

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This paper cites NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights

Reference 10

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This paper cites B-cos Net- works: Alignment is All We Need for Interpretability, 2022.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations B-cos Net- works: Alignment is All We Need for Interpretability, 2022

Reference 11

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Observation 55be846a-9843-443e-aa79-921f5c9ae41e · outbound

This paper cites Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks

Reference 12

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This paper cites Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods

Reference 13

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This paper cites True to the Model or True to the Data?.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations True to the Model or True to the Data?

Reference 14

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This paper cites Kernel Feature Selection via Conditional Covariance Minimization.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Kernel Feature Selection via Conditional Covariance Minimization

Reference 15

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This paper cites Explaining by Removing: A Unified Framework for Model Explanation,.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Explaining by Removing: A Unified Framework for Model Explanation,

Reference 16

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This paper cites Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

Reference 17

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This paper cites ImageNet: A large-scale hierarchical im- age database.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations ImageNet: A large-scale hierarchical im- age database

Reference 18

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This paper cites On the lipschitz constant of deep networks and double de- scent.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On the lipschitz constant of deep networks and double de- scent

Reference 19

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This paper cites On the Similarity between the Laplace and Neural Tangent Kernels, 2020.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On the Similarity between the Laplace and Neural Tangent Kernels, 2020

Reference 20

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Observation 39ecbfd3-9e09-4ad4-b2cd-8b4db70cad67 · outbound

This paper cites Large-scale Nonlinear Variable Selection via Kernel Random Features.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Large-scale Nonlinear Variable Selection via Kernel Random Features

Reference 21

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This paper cites Spectral analysis based on signal depen- dent transformation.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Spectral analysis based on signal depen- dent transformation

Reference 22

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This paper cites Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations

Reference 23

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This paper cites Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD

Reference 24

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This paper cites Deep Residual Learning for Image Recognition.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Deep Residual Learning for Image Recognition

Reference 25

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This paper cites A Benchmark for Interpretability Methods in Deep Neural Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 26

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On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Unresolved cited work

Reference 27

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This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 28

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This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 29

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On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Sriperumbudur

Reference 30

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This paper cites Guided Integrated Gradients: An Adaptive Path Method for Removing Noise.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Guided Integrated Gradients: An Adaptive Path Method for Removing Noise

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This paper cites Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps,.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps,

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This paper cites Sch ¨utt, Sven D ¨ahne, Dumitru Er- han, and Been Kim.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Sch ¨utt, Sven D ¨ahne, Dumitru Er- han, and Been Kim

Reference 33

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This paper cites Captum: A unified and generic model interpretability library for PyTorch, 2020.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Captum: A unified and generic model interpretability library for PyTorch, 2020

Reference 34

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This paper cites Cartoon Explanations of Image Classifiers,.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Cartoon Explanations of Image Classifiers,

Reference 35

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This paper cites Explaining Image Clas- sifiers with Multiscale Directional Image Representation,.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Explaining Image Clas- sifiers with Multiscale Directional Image Representation,

Reference 36

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This paper cites Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

Reference 37

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This paper cites Gershman, and Finale Doshi-Velez.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Gershman, and Finale Doshi-Velez

Reference 38

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This paper cites Lundberg and Su-In Lee.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Lundberg and Su-In Lee

Reference 39

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This paper cites Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks

Reference 40

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This paper cites Cartoon Explanations of Image Classifiers.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Cartoon Explanations of Image Classifiers

Reference 41

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This paper cites Characterizing the Spectrum of the NTK via a Power Series Expansion.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Characterizing the Spectrum of the NTK via a Power Series Expansion

Reference 42

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This paper cites Sensitivity and Generalization in Neural Networks: an Empirical Study.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Sensitivity and Generalization in Neural Networks: an Empirical Study

Reference 43

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This paper cites Mathematical theory of deep learning, 2024.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Mathematical theory of deep learning, 2024

Reference 44

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This paper cites Hamprecht, Yoshua Bengio, and Aaron Courville.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Hamprecht, Yoshua Bengio, and Aaron Courville

Reference 45

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This paper cites How Reliable and Stable are Explanations of XAI Methods?.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations How Reliable and Stable are Explanations of XAI Methods?

Reference 46

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This paper cites A case for new neural network smoothness constraints.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations A case for new neural network smoothness constraints

Reference 47

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This paper cites On Spectral Properties of Gradient-Based Explanation Meth- ods.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On Spectral Properties of Gradient-Based Explanation Meth- ods

Reference 48

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This paper cites Best of both worlds: local and global explanations with human-understandable concepts,.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Best of both worlds: local and global explanations with human-understandable concepts,

Reference 49

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This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Reference 50

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Observation b08a5f10-bfc0-453c-aa9f-16e1c268b26b · outbound

This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 51

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This paper cites Reverse Engineering the Neural Tangent Kernel.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Reverse Engineering the Neural Tangent Kernel

Reference 52

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Observation 1d4f458f-4179-477a-82a0-5678c2b209c0 · outbound

This paper cites On the Spectral Bias of Neural Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On the Spectral Bias of Neural Networks

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This paper cites SmoothGrad: removing noise by adding noise.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations SmoothGrad: removing noise by adding noise

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This paper cites Striving for Simplicity: The All Convolutional Net.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Striving for Simplicity: The All Convolutional Net

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This paper cites Stop explaining black box machine learn- ing models for high stakes decisions and use interpretable models instead.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Stop explaining black box machine learn- ing models for high stakes decisions and use interpretable models instead

Reference 56

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Observation 61dc7dfe-a92a-4af2-9d50-5940f3887172 · outbound

This paper cites One pixel attack for fooling deep neural networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations One pixel attack for fooling deep neural networks

Reference 57

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This paper cites Best of both worlds: local and global explanations with human-understandable concepts.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Best of both worlds: local and global explanations with human-understandable concepts

Reference 58

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This paper cites On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions

Reference 59

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This paper cites From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation

Reference 60

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This paper cites Feature Importance Ranking for Deep Learning.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Feature Importance Ranking for Deep Learning

Reference 61

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 62

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This paper cites Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Reference 63

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On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Benchmarking Attribution Methods with Relative Feature Importance

Reference 64

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On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Unresolved cited work

Reference 65

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This paper cites One pixel attack for fooling deep neural networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations One pixel attack for fooling deep neural networks

Reference 67

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This paper cites Axiomatic Attribution for Deep Networks.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Axiomatic Attribution for Deep Networks

Reference 68

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This paper cites Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 72

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This paper cites In this degenerate case, no root is present.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations In this degenerate case, no root is present

Reference 75

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This paper cites In a neighborhood of such a point, by a first-order Taylor expansion, ∆(τ ) ≈ α(τ − τ ∗), for some α ̸= 0 and τ close to τ ∗.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations In a neighborhood of such a point, by a first-order Taylor expansion, ∆(τ ) ≈ α(τ − τ ∗), for some α ̸= 0 and τ close to τ ∗

Reference 76

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Observation cbe652b4-87af-4b64-917a-cb88b1dc70fb · outbound

This paper cites The (Un)reliability of saliency methods.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations The (Un)reliability of saliency methods

Reference 2017

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This paper cites Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

Reference 2018

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This paper cites Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps

Reference 2019

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Observation be264f38-478a-4b9f-b0ee-5db4c7b054d3 · outbound

This paper cites Evaluating and Aggregating Feature-based Model Explanations.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Evaluating and Aggregating Feature-based Model Explanations

Reference 2020

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This paper cites Explaining by Removing: A Unified Framework for Model Explanation.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Explaining by Removing: A Unified Framework for Model Explanation

Reference 2022

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Observation e95df0b0-23f3-4773-a5cd-9998af3532a8 · outbound

This paper cites Explaining Image Classifiers with Multiscale Directional Image Representation.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Explaining Image Classifiers with Multiscale Directional Image Representation

Reference 2023

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