Conditional image generation is the task of generating diverse images using
class label information. Although many conditional Generative Adversarial
Networks (GAN) have shown realistic results, such methods consider pairwise
relations between the embedding of an image and the embedding of the
corresponding label (data-to-class relations) as the conditioning losses. In
this paper, we propose ContraGAN that considers relations between multiple
image embeddings in the same batch (data-to-data relations) as well as the
data-to-class relations by using a conditional contrastive loss. The
discriminator of ContraGAN discriminates the authenticity of given samples and
minimizes a contrastive objective to learn the relations between training
images. Simultaneously, the generator tries to generate realistic images that
deceive the authenticity and have a low contrastive loss. The experimental
results show that ContraGAN outperforms state-of-the-art-models by 7.3% and
7.7% on Tiny ImageNet and ImageNet datasets, respectively. Besides, we
experimentally demonstrate that contrastive learning helps to relieve the
overfitting of the discriminator. For a fair comparison, we re-implement twelve
state-of-the-art GANs using the PyTorch library. The software package is
available at this https URL

本文提出了 ContraGAN 的方法，利用条件对比损失函数，考虑同一批次中多个图像嵌入之间的关系 (data-to-data relations) 和数据与类的关系 (data-to-class relations)。实验结果表明，ContraGAN 在 Tiny ImageNet 和 ImageNet 数据集上比现有模型分别提高了 7.3% 和 7.7% 的性能表现，并且对比学习有助于解决判别器过拟合的问题。

ContraGAN: 基于对比学习的条件图像生成

ContraGAN: Contrastive Learning for Conditional Image Generation

Existing metric learning losses can be categorized into two classes:
pair-based and proxy-based losses. The former class can leverage fine-grained
semantic relations between data points, but slows convergence in general due to
its high training complexity. In contrast, the latter class enables fast and
reliable convergence, but cannot consider the rich data-to-data relations. This
paper presents a new proxy-based loss that takes advantages of both pair- and
proxy-based methods and overcomes their limitations. Thanks to the use of
proxies, our loss boosts the speed of convergence and is robust against noisy
labels and outliers. At the same time, it allows embedding vectors of data to
interact with each other in its gradients to exploit data-to-data relations.
Our method is evaluated on four public benchmarks, where a standard network
trained with our loss achieves state-of-the-art performance and most quickly
converges.

该研究提出了一种新的代理损失方法，它结合了基于对和代理的方法的优点并克服了它们的局限性，同时具有更快的收敛速度和对嘈杂标签和离群值的鲁棒性，能充分利用数据之间的关系，其中该方法在四个公共基准测试中表现出最先进的性能和最快的收敛速度。