Search Results for author:
Found 4 papers, 3 papers with code
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories.
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
In the analysis and ablation study, we demonstrate that our method compared with previous work can effectively increase the diversity of experts, significantly reduce the variance of the model, and improve recognition accuracy.
Ranked #5 on
Long-tail Learning
on CIFAR-10-LT (ρ=50)
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model.
Ranked #1 on
Data Augmentation
on ImageNet
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability.
