4 code implementations • 13 Oct 2021 • Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann Lecun
Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72. 3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning.
no code implementations • 1 Jan 2021 • Yen-Chi Hsu, Cheng-Yao Hong, Wan-Cyuan Fan, Ding-Jie Chen, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task.
no code implementations • None 2019 • Yen-Chi Hsu, Cheng-Yao Hong, Ding-Jie Chen, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC).
Ranked #17 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 28 Oct 2019 • Yen-Chi Hsu, Cheng-Yao Hong, Wan-Cyuan Fan, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
With the development of deep learning, standard classification problems have achieved good results.
Fine-Grained Image Classification Fine-Grained Visual Recognition