17 citations · 49 across the 8 of their papers we have counts for
6 papers · 1 filter
Learning After Model Deployment
Derda Kaymak, Gyuhak Kim, Tomoya Kaichi +2
In classic supervised learning, once a model is deployed in an application, it is fixed. No updates will be made to it during the application. This is inappropriate for many dynami…
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…
Learnability and Algorithm for Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi +1
This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or cl…
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…