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Found 4 papers, 1 papers with code
Decoupled Contrastive Learning
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.
Natural World Distribution via Adaptive Confusion Energy Regularization
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.
ACE: Adaptive Confusion Energy for Natural World Data Distribution
With the development of deep learning, standard classification problems have achieved good results.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Fine-Grained Visual Classification with Batch Confusion Norm
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
