Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions

• URL: http://arxiv.org/abs/2402.17065v2
• Date: Sun, 16 Jun 2024 22:11:56 GMT
• Title: Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
• Authors: Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad H. Danesh, Li Fuxin,
• Abstract summary: GANs tend to favor classes with more samples, leading to the generation of low-quality and less diverse samples in tail classes. We propose a straightforward yet effective method for knowledge sharing, allowing tail classes to borrow from the rich information from classes with more abundant training data. Experiments on several long-tail benchmarks and GAN architectures demonstrate a significant improvement over existing methods in both the diversity and fidelity of the generated images.
• Score: 10.946446480162148
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remains fairly unexplored. In the presence of imbalanced multi-class training data, GANs tend to favor classes with more samples, leading to the generation of low-quality and less diverse samples in tail classes. In this study, we aim to improve the training of class-conditional GANs with long-tailed data. We propose a straightforward yet effective method for knowledge sharing, allowing tail classes to borrow from the rich information from classes with more abundant training data. More concretely, we propose modifications to existing class-conditional GAN architectures to ensure that the lower-resolution layers of the generator are trained entirely unconditionally while reserving class-conditional generation for the higher-resolution layers. Experiments on several long-tail benchmarks and GAN architectures demonstrate a significant improvement over existing methods in both the diversity and fidelity of the generated images. The code is available at https://github.com/khorrams/utlo.

Related papers

• Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts [10.99232053983369]
  We propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning. We execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework.
  arXiv  Detail & Related papers  (2023-08-31T10:12:32Z)
• FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs [24.18718734850797]
  Data-Efficient GANs (DE-GANs) aim to learn generative models with a limited amount of training data. Contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs. We propose FakeCLR, which only applies contrastive learning on fake samples.
  arXiv  Detail & Related papers  (2022-07-18T14:23:38Z)
• Collapse by Conditioning: Training Class-conditional GANs with Limited Data [109.30895503994687]
  We propose a training strategy for conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning. Our training strategy starts with an unconditional GAN and gradually injects conditional information into the generator and the objective function. The proposed method for training cGANs with limited data results not only in stable training but also in generating high-quality images.
  arXiv  Detail & Related papers  (2022-01-17T18:59:23Z)
• Class Balancing GAN with a Classifier in the Loop [58.29090045399214]
  We introduce a novel theoretically motivated Class Balancing regularizer for training GANs. Our regularizer makes use of the knowledge from a pre-trained classifier to ensure balanced learning of all the classes in the dataset. We demonstrate the utility of our regularizer in learning representations for long-tailed distributions via achieving better performance than existing approaches over multiple datasets.
  arXiv  Detail & Related papers  (2021-06-17T11:41:30Z)
• Regularizing Generative Adversarial Networks under Limited Data [88.57330330305535]
  This work proposes a regularization approach for training robust GAN models on limited data. We show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data.
  arXiv  Detail & Related papers  (2021-04-07T17:59:06Z)
• Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly [114.81028176850404]
  Training generative adversarial networks (GANs) with limited data generally results in deteriorated performance and collapsed models. We decompose the data-hungry GAN training into two sequential sub-problems. Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems.
  arXiv  Detail & Related papers  (2021-02-28T05:20:29Z)
• Efficient Conditional GAN Transfer with Knowledge Propagation across Classes [85.38369543858516]
  CGANs provide new opportunities for knowledge transfer compared to unconditional setup. New classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. New GAN transfer method explicitly propagates the knowledge from the old classes to the new classes.
  arXiv  Detail & Related papers  (2021-02-12T18:55:34Z)
• Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample [6.243995448840211]
  We present a novel non-adversarial generative method - Clustered Optimization of LAtent space (COLA) In the full data regime, our method is capable of generating diverse multi-class images with no supervision. In the small-data regime, where only a small sample of labeled images is available for training with no access to additional unlabeled data, our results surpass state-of-the-art GAN models trained on the same amount of data.
  arXiv  Detail & Related papers  (2020-11-25T18:47:27Z)
• Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks [31.073558420480964]
  We propose a method to restore the balance in imbalanced images, by coalescing two concurrent methods. In our model, generative and discriminative networks play a novel competitive game. The coalescing of capsule-GAN is effective at recognizing highly overlapping classes with much fewer parameters compared with the convolutional-GAN.
  arXiv  Detail & Related papers  (2020-04-05T12:36:06Z)
• Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs [104.85633684716296]
  We show that simple fine-tuning of GANs with frozen lower layers of the discriminator performs surprisingly well. This simple baseline, FreezeD, significantly outperforms previous techniques used in both unconditional and conditional GANs.
  arXiv  Detail & Related papers  (2020-02-25T15:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.