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Found 14 papers, 7 papers with code
Forward Learning for Gradient-based Black-box Saliency Map Generation
Gradient-based saliency maps are widely used to explain deep neural network decisions.
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
Discovering biased subgroups is the key to understanding models' failure modes and further improving models' robustness.
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation.
Bag of Tricks to Boost Adversarial Transferability
In this work, we find that several tiny changes in the existing adversarial attacks can significantly affect the attack performance, \eg, the number of iterations and step size.
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
Structure Invariant Transformation for better Adversarial Transferability
In this work, we find that the existing input transformation based attacks transform the input image globally, resulting in limited diversity of the transformed images.
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines.
Diversifying the High-level Features for better Adversarial Transferability
Incorporated into the input transformation-based attacks, DHF generates more transferable adversarial examples and outperforms the baselines with a clear margin when attacking several defense models, showing its generalization to various attacks and high effectiveness for boosting transferability.
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
Improving Adversarial Transferability with Scheduled Step Size and Dual Example
Motivated by this finding, we argue that the information of adversarial perturbations near the benign sample, especially the direction, benefits more on the transferability.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
Triangle Attack: A Query-efficient Decision-based Adversarial Attack
Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label.
Noise Optimization for Artificial Neural Networks
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work.
