#catastrophic forgetting
12 papers · 1 filter
DECODE: Tackling Representation and Decision Degradation in Continual AI-Generated Image Detection
Zihao Cai, Xinghan Li, Ruiyan Yang +3
The paper introduces DECODE, a framework that addresses both representation and decision-level forgetting in continual learning for AI-generated image detection, using subspace div…
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Zekai Chen, Haodong Lu, Shihao Li +5
The paper introduces FedOGL, a framework for federated multimodal graph learning that mitigates catastrophic forgetting by preserving semantic and structural memory through client-…
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
Ashmith Atmuri, Yashaswini Rao Bhogarajula
The paper presents CMP, a continual‑learning system that encodes inputs as sparse relational codes, stores them in a two‑tier competitive memory, and learns via local updates witho…
Gate-Zero Growth: A Geometric Framework for Function-Preserving Continual Learning
Dante Lok
The paper proposes gate-zero growth, a function‑preserving operator that adds new residual blocks via a zero‑initialized gate, enabling controlled function drift and near‑zero forg…
Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection
Mingyue Zeng, De Cheng, Zhipeng Xu +3
The paper introduces Symbiosis-Inspired Knowledge Distillation (SIKD), a method for incremental object detection that leverages spatial and semantic relationships between old and n…
REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Cheng-Kang Chou, Ming-To Chuang, Ke-Han Lu +2
The paper investigates drift in model-generated timestamps for autoregressive ASR systems and introduces REDDIT, a replay‑based distribution editing post‑training method that corre…