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Found 7 papers, 3 papers with code
GDA: Generalized Diffusion for Robust Test-time Adaptation
For vision tasks, recent studies have shown that test-time adaptation employing diffusion models can achieve state-of-the-art accuracy improvements on OOD samples by generating new samples that align with the model's domain without the need to modify the model's weights.
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Test-time defenses solve these issues but most existing test-time defenses require adapting the model weights, therefore they do not work on frozen models and complicate model memory management.
CARBEN: Composite Adversarial Robustness Benchmark
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e. g., perturbations bounded in Lp ball.
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation.
Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$ norm to composite semantic perturbations, such as Hue, Saturation, Brightness, Contrast, and Rotation.
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Learning to classify time series with limited data is a practical yet challenging problem.
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data.
BIG-bench Machine Learning
Diabetic Retinopathy Detection
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