1 citations · 1 across the 8 of their papers we have counts for
8 papers
Synesthesia via Direct Latent Augmentation:Bypassing the Decode-Encode Loop for Cross-Modal Distillation
Cristian Sbrolli, Nicolas Michel, Matteo Matteucci +1
While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent…
Continual Distillation of Teachers from Different Domains
Nicolas Michel, Maorong Wang, Jiangpeng He +1
Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a stu…
Adversarial Training from Mean Field Perspective
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Although adversarial training is known to be effective against adversarial examples, training dynamics are not well understood. In this study, we present the first theoretical anal…
Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Adversarial training is one of the most effective defenses against adversarial attacks, but it incurs a high computational cost. In this study, we present the first theoretical ana…
Wide Two-Layer Networks can Learn from Adversarial Perturbations
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Adversarial examples have raised several open questions, such as why they can deceive classifiers and transfer between different models. A prevailing hypothesis to explain these ph…
Language-guided Detection and Mitigation of Unknown Dataset Bias
Zaiying Zhao, Soichiro Kumano, Toshihiko Yamasaki
Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers trained o…