6 citations · 8 across the 5 of their papers we have counts for
5 papers
Rethinking Momentum Knowledge Distillation in Online Continual Learning
Nicolas Michel, Maorong Wang, Ling Xiao +1
Online Continual Learning (OCL) addresses the problem of training neural networks on a continuous data stream where multiple classification tasks emerge in sequence. In contrast to…
MetaMixer: A Regularization Strategy for Online Knowledge Distillation
Maorong Wang, Ling Xiao, Toshihiko Yamasaki
Online knowledge distillation (KD) has received increasing attention in recent years. However, while most existing online KD methods focus on developing complicated model structure…
Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval
Ling Xiao, Toshihiko Yamasaki
Fine-grained fashion retrieval searches for items that share a similar attribute with the query image. Most existing methods use a pre-trained feature extractor (e.g., ResNet 50) t…
Semi-supervised Fashion Compatibility Prediction by Color Distortion Prediction
Ling Xiao, Toshihiko Yamasaki
Supervised learning methods have been suffering from the fact that a large-scale labeled dataset is mandatory, which is difficult to obtain. This has been a more significant issue…
SAT: Self-adaptive training for fashion compatibility prediction
Ling Xiao, Toshihiko Yamasaki
This paper presents a self-adaptive training (SAT) model for fashion compatibility prediction. It focuses on the learning of some hard items, such as those that share similar color…