Loss smoothing interpolates between source and target objectives during adaptation and improves performance across supervised shifts, vision fine-tuning, RL, and LM tasks.
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BRAIN uses bias-mitigation continual learning with a new de-bias contrastive loss and angular forgetting mitigation to achieve SOTA performance on vision-brain understanding benchmarks despite brain signal inconsistencies across sessions.
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Loss Smoothing for Stable Adaptation Under Distribution Shift
Loss smoothing interpolates between source and target objectives during adaptation and improves performance across supervised shifts, vision fine-tuning, RL, and LM tasks.
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BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding
BRAIN uses bias-mitigation continual learning with a new de-bias contrastive loss and angular forgetting mitigation to achieve SOTA performance on vision-brain understanding benchmarks despite brain signal inconsistencies across sessions.