8 citations · 8 across the 3 of their papers we have counts for
4 papers
Incremental Learning with Differentiable Architecture and Forgetting Search
James Seale Smith, Zachary Seymour, Han-Pang Chiu
As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to i…
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients
Yen-Chang Hsu, James Smith, Yilin Shen +2
Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. A vast number of methods have been developed for…
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
James Smith, Yen-Chang Hsu, Jonathan Balloch +3
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgett…
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
James Smith, Jonathan Balloch, Yen-Chang Hsu +1
Rehearsal is a critical component for class-incremental continual learning, yet it requires a substantial memory budget. Our work investigates whether we can significantly reduce t…