Network Interpretation
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Fooling Neural Network Interpretations via Adversarial Model Manipulation
rmrisforbidden/Fooling_Neural_Network-Interpretations
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NeurIPS 2019
We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original models, e. g., VGG19, ResNet50, and DenseNet121.
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
salesforce/lavis
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Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting.
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness
a1noack/interp_regularization
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We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations.
Proper Network Interpretability Helps Adversarial Robustness in Classification
AkhilanB/Proper-Interpretability
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ICML 2020
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.
Attribution Preservation in Network Compression for Reliable Network Interpretation
GeondoPark/attribute-preserve
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NeurIPS 2020
Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely on two important techniques: input attribution for hindsight analysis and network compression to reduce its size for edge-computing.
DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
uwdb/DeepEverest
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We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network.
Towards More Robust Interpretation via Local Gradient Alignment
However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods.
