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Found 7 papers, 4 papers with code
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning.
Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts
Generative models have demonstrated remarkable potential in generating visually impressive content from textual descriptions.
Robust Contrastive Learning With Theory Guarantee
Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information.
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals (e. g., finding adversarial examples that maximize the model losses for simultaneously attacking multiple models).
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to `contrast' between data and class representation in the latent space.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
An important technique of this approach is to control the transferability of adversarial examples among ensemble members.
Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application.
