5 accepted papers
Fully Few-shot Class-incremental Audio Classification (FFCAC) is challenging since the training samples are limited both in the incremental sessions and in the base session. Existing few-shot learning methods suffer from catastrophic forgetting and overfitting when applied to FFCAC.Pre-trained Audio
In deep reinforcement learning (DRL), the presence of dormant neurons leads to a significant reduction in network capacity, which results in sub-optimal performance and limited sample efficiency. Existing training techniques, especially those relying on periodic resetting (PR), exacerbate this issue
Online continual learning (OCL) is a strict setting of continual learning (CL), where the OCL agent faces a never-ending data stream and encounters each new sample only once. An OCL agent suffers more serious catastrophic forgetting (i.e., forgetting previous knowledge of old classes) than a CL agen…
Catastrophic forgetting has become an intractable problem in the continual learning setting because previous data is not accessible when training. To mitigate this problem, memory-based continual learning methods replay previous data from a fixed-size memory buffer. Reservoir sampling, which can sam…
A class-incremental learning agent learns online with a neverending stream of data in one training epoch. In this setting, the agent suffers from severe catastrophic forgetting due to the absence of data from the observed classes after learning data from new classes. Besides, the prototypes rapidly…