Latent-based Diffusion Model for Long-tailed Recognition
Pengxiao Han Changkun Ye Jieming Zhou Jing Zhang Jie Hong Xuesong Li
Long-tailed imbalance distribution is a common issue in practical computer
vision applications. Previous works proposed methods to address this problem,
which can be categorized into several classes: re-sampling, re-weighting,
transfer learning, and feature augmentation. In recent years, diffusion models
have shown an impressive generation ability in many sub-problems of deep
computer vision. However, its powerful generation has not been explored in
long-tailed problems. We propose a new approach, the Latent-based Diffusion
Model for Long-tailed Recognition (LDMLR), as a feature augmentation method to
tackle the issue. First, we encode the imbalanced dataset into features using
the baseline model. Then, we train a Denoising Diffusion Implicit Model (DDIM)
using these encoded features to generate pseudo-features. Finally, we train the
classifier using the encoded and pseudo-features from the previous two steps.
The model's accuracy shows an improvement on the CIFAR-LT and ImageNet-LT
datasets by using the proposed method.