Advances in generative modeling based on GANs has motivated the community to find their use beyond image generation and editing tasks. In particular, several recent works have shown that GAN representations can be re-purposed for discriminative tasks such as part segmentation, especially when training data is limited. But how do these improvements stack-up against recent advances in self-supervised learning? Motivated by this we present an alternative approach based on contrastive learning and compare their performance on standard few-shot part segmentation benchmarks. Our experiments reveal that not only do the GAN-based approach offer no significant performance advantage, their multi-step training is complex, nearly an order-of-magnitude slower, and can introduce additional bias. These experiments suggest that the inductive biases of generative models, such as their ability to disentangle shape and texture, are well captured by standard feed-forward networks trained using contrastive learning. These experiments suggest that the inductive biases present in current generative models, such as their ability to disentangle shape and texture, are well captured by standard feed-forward networks trained using contrastive learning.
翻译:以GANs为基础的基因建模进步激励了社区发现这些建模除了图像生成和编辑任务之外还利用了图像生成和编辑任务。 特别是,最近的一些工程表明,GAN的表示方式可以重新用于部分分割等歧视性任务,特别是在培训数据有限的情况下。但是,这些改进是如何堆叠起来的,与最近自我监督学习的进展相比呢?我们为此提出一种基于对比学习的替代方法,并根据标准的微小分块分化基准比较其性能。我们的实验表明,GAN的表示方式不仅没有提供显著的性能优势,而且其多步制培训是复杂的,几乎是放大的顺序放慢,并且可以引入更多的偏差。这些实验表明,基因模型的暗示偏差,如其分解形状和纹理的能力,被利用对比学习训练的标准进化前网络所捕捉到。这些实验表明,目前基因化模型中存在的含色的偏差,例如分解形状和纹理的能力,被通过对比学习训练的标准进向前网络所捕捉到。