改进 SCGAN 的相似性约束并学习更好的解耦表示
Improving SCGAN's Similarity Constraint and Learning a Better Disentangled Representation
AI总结：
本文通过揭示 SCGAN 相似性约束的对比损失本质，引入 SSIM 与对比损失原则改进模型，提升了 FID、FactorVAE 表现及泛化能力。
摘要：
SCGAN 在生成对抗网络中将生成图像与条件之间的相似性约束作为正则化项加入。相似性约束充当导师，指导生成器网络理解基于条件的表示差异。我们在更深层次上理解了 SCGAN 的工作机制。这一理解使我们认识到，相似性约束的作用类似于对比损失函数。我们认为，一个具有高度理解能力和智能的模型应当像人类一样，根据图像的结构和高级特征来衡量图像之间的相似性。为获得改进模型，我们对 SCGAN 实施了两项主要修改：使用 SSIM 衡量图像间的相似性，并将对比损失原则应用于相似性约束。改进后的模型在 FID 和 FactorVAE 指标上表现更好。与其他模型相比，该模型还具有更优的泛化能力。关键词：生成对抗网络、无监督学习、解耦表示学习、对比解耦、SSIM。
英文摘要：
SCGAN adds a similarity constraint between generated images and conditions as a regularization term on generative adversarial networks. Similarity constraint works as a tutor to instruct the generator network to comprehend the difference of representations based on conditions. We understand how SCGAN works on a deeper level. This understanding makes us realize that the similarity constraint functions like the contrastive loss function. We believe that a model with high understanding and intelligence measures the similarity between images based on their structure and high level features, just like humans do. Two major changes we applied to SCGAN in order to make a modified model are using SSIM to measure similarity between images and applying contrastive loss principles to the similarity constraint. The modified model performs better using FID and FactorVAE metrics. The modified model also has better generalisability compared to other models. Keywords Generative Adversarial Nets, Unsupervised Learning, Disentangled Representation Learning, Contrastive Disentanglement, SSIM
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
