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Found 6 papers, 5 papers with code
AutoVP: An Automated Visual Prompting Framework and Benchmark
To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark.
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations.
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks.
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.
