21 citations · 125 across the 18 of their papers we have counts for
6 papers · 1 filter
Online Continual Learning on Hierarchical Label Expansion
Byung Hyun Lee, Okchul Jung, Jonghyun Choi +1
Continual learning (CL) enables models to adapt to new tasks and environments without forgetting previously learned knowledge. While current CL setups have ignored the relationship…
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
Xinyue Ma, Suyeon Jeong, Minjia Zhang +3
Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hi…
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference
Hyunseo Koh, Dahyun Kim, Jung-Woo Ha +1
Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups. While a handful of work do propose new continual…
Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning
Dahyun Kim, Jonghyun Choi
Self-supervised learning is a promising unsupervised learning framework that has achieved success with large floating point networks. But such networks are not readily deployable t…
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Soobee Lee, Minindu Weerakoon, Jonghyun Choi +3
Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks.…
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence
Dahyun Kim, Jihwan Bae, Yeonsik Jo +1
Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned w…