RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
Tiny imagenet visual recognition challenge.CS 231N, 7(7):3
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
STARS augments BN-guided data synthesis with relational consistency alignment and tail-aware regularization for ANN-to-SNN data-free KD, reporting gains up to 4.6% on CIFAR-10 and 6.7% on CIFAR-100.
citing papers explorer
-
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
-
STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
STARS augments BN-guided data synthesis with relational consistency alignment and tail-aware regularization for ANN-to-SNN data-free KD, reporting gains up to 4.6% on CIFAR-10 and 6.7% on CIFAR-100.