LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions | Paper Citation Record

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This paper cites Quantifying attention flow in transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Quantifying attention flow in transformers

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This paper cites AttnLRP: Attention- aware layer-wise relevance propagation for transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions AttnLRP: Attention- aware layer-wise relevance propagation for transformers

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This paper cites XAI for trans- formers: Better explanations through conservative propaga- tion.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions XAI for trans- formers: Better explanations through conservative propaga- tion

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Unresolved cited work

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This paper cites Anders, David Neumann, Talmaj Marinc, Wojciech Samek, Klaus-Robert M ¨uller, and Sebastian La- puschkin.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Anders, David Neumann, Talmaj Marinc, Wojciech Samek, Klaus-Robert M ¨uller, and Sebastian La- puschkin

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This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

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This paper cites BEiT: BERT Pre-Training of Image Transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions BEiT: BERT Pre-Training of Image Transformers

Reference 7

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This paper cites Tenenbaum, and Boris Katz.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Tenenbaum, and Boris Katz

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This paper cites ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

Reference 9

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This paper cites Are we done with ImageNet?.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Are we done with ImageNet?

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This paper cites Alabdulmohsin, and Filip Pavetic.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Alabdulmohsin, and Filip Pavetic

Reference 11

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This paper cites Layer-wise rel- evance propagation for deep neural network architectures.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Layer-wise rel- evance propagation for deep neural network architectures

Reference 12

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This paper cites De- coupling pixel flipping and occlusion strategy for consistent xai benchmarks.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions De- coupling pixel flipping and occlusion strategy for consistent xai benchmarks

Reference 13

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This paper cites On iden- tifiability in transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions On iden- tifiability in transformers

Reference 14

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This paper cites Emerg- ing properties in self-supervised vision transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Emerg- ing properties in self-supervised vision transformers

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This paper cites Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Transformer inter- pretability beyond attention visualization

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This paper cites Optimizing Relevance Maps of Vision Transformers Improves Robustness.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Optimizing Relevance Maps of Vision Transformers Improves Robustness

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This paper cites Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models

Reference 19

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This paper cites Generative pre- training from pixels.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Generative pre- training from pixels

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This paper cites Training deep nets with sublinear memory cost, 2016.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Training deep nets with sublinear memory cost, 2016

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This paper cites Learning to Estimate Shapley Values with Vision Transformers.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Learning to Estimate Shapley Values with Vision Transformers

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This paper cites AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Imagenet: A large-scale hierarchical image database

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions An image is worth 16x16 words: Transformers for image recognition at scale

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This paper cites Use hirescam in- stead of grad-cam for faithful explanations of convolutional neural networks.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Use hirescam in- stead of grad-cam for faithful explanations of convolutional neural networks

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Explain to not forget: Defending against catas- trophic forgetting with xai

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Eva: Exploring the limits of masked visual representation learning at scale

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions EVA-02: A Visual Representation for Neon Genesis

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Adaptive token sampling for efficient vision transformers

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Craft: Concept recursive activation factoriza- tion for explainability

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions G ´allego, and Marta R

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Self-attention at- tribution: Interpreting information interactions inside trans- former

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Benchmarking neu- ral network robustness to common corruptions and perturba- tions

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions The many faces of robustness: A critical analysis of out-of-distribution generalization

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Natural adversarial examples.CVPR,

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Transferable Adversarial Attack based on Integrated Gradients

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Ex- plaining convolutional neural networks using softmax gradi- ent layer-wise relevance propagation

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Layercam: Exploring hierarchical class activation maps for localization

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Dense Text-to-Image Generation with Attention Modulation

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Investigating the influence of noise and distractors on the interpretation of neural networks

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Attention is not only a weight: Analyzing trans- formers with vector norms

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Incorporating Residual and Normalization Layers into Analysis of Masked Language Models

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Towards Faithful Model Explanation in NLP: A Survey

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions SkipPLUS: Skip the first few layers to better explain vision transform- ers

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions GlobEnc: Quantifying global token attribution by incorporating the whole encoder layer in transformers

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Modarressi, Hosein Mohebbi, and Mohammad Taher Pilehvar

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions De- compX: Explaining transformers decisions by propagating token decomposition

Reference 52

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Ex- plaining nonlinear classification decisions with deep taylor decomposition

Reference 53

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This paper cites Comparing automatic and human evaluation of local explanations for text classification.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Comparing automatic and human evaluation of local explanations for text classification

Reference 54

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Decompose-and-compose: A composi- tional approach to mitigating spurious correlation

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LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure

Reference 56

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Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 62

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Reference 63

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Reference 64

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Reference 67

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Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Reference 76

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Reference 77

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Reference 78

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Reference 79

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Reference 80

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Reference 81

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Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Reference 88

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Reference 89

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Reference 91

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Reference 92

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Reference 93

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Reference 98

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Reference 99

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Reference 102

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