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Found 8 papers, 3 papers with code
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
Adversarial robustness often comes at the cost of degraded accuracy, impeding the real-life application of robust classification models.
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes.
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off
However, standard learning models often suffer from an accuracy-robustness trade-off, which is a limitation that must be overcome in the control of safety-critical systems that require both high performance and rigorous robustness guarantees.
Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
Diffusion models power a vast majority of text-to-audio (TTA) generation methods.
Ranked #10 on
Audio Generation
on AudioCaps
Initial State Interventions for Deconfounded Imitation Learning
Imitation learning suffers from causal confusion.
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to their unacceptably severe clean accuracy penalties.
Ranked #1 on
Adversarial Robustness
on CIFAR-100
(using extra training data)
Efficient Global Optimization of Two-layer ReLU Networks: Quadratic-time Algorithms and Adversarial Training
We apply the robust convex optimization theory to convex training and develop convex formulations that train ANNs robust to adversarial inputs.
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program.
