53 citations · 183 across the 18 of their papers we have counts for
6 papers · 1 filter
Parameter-Level Soft-Masking for Continual Learning
Tatsuya Konishi, Mori Kurokawa, Chihiro Ono +3
Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Although several techniques have achieved lea…
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi +2
As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detec…
A Theoretical Study on Solving Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi +2
Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL settings, class incremental learning (CIL) and task incremental learning (TIL). A major c…
A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
Gyuhak Kim, Zixuan Ke, Bing Liu
This paper studies class incremental learning (CIL) of continual learning (CL). Many approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most methods in…
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data
Zixuan Ke, Mohammad Kachuee, Sungjin Lee
In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category. In this paper, we study mu…
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
Zixuan Ke, Bing Liu, Xingchang Huang
Existing research on continual learning of a sequence of tasks focused on dealing with catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared…