SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
Learning multiple layers of features from tiny images
6 Pith papers cite this work. Polarity classification is still indexing.
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DDG dynamically adjusts perturbation magnitude and supervision strength in fast adversarial training according to sample confidence at the ground-truth class, mitigating catastrophic overfitting and the robustness-accuracy trade-off.
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
ComMark embeds covert watermarks in models using frequency-domain compressed samples and simulated attacks, claiming state-of-the-art covertness and robustness across image, speech, text, and video tasks.
citing papers explorer
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SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
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Mitigating Error Amplification in Fast Adversarial Training
DDG dynamically adjusts perturbation magnitude and supervision strength in fast adversarial training according to sample confidence at the ground-truth class, mitigating catastrophic overfitting and the robustness-accuracy trade-off.
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Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
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From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
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ComMark: Covert and Robust Black-Box Model Watermarking with Compressed Samples
ComMark embeds covert watermarks in models using frequency-domain compressed samples and simulated attacks, claiming state-of-the-art covertness and robustness across image, speech, text, and video tasks.
- Concept-wise Attention for Fine-grained Concept Bottleneck Models