用双空间编码加速GAN训练，提升生成效率与创意潜力。
Dual Space Training for GANs: A Pathway to Efficient and Creative Generative Models
- 在数据的可逆编码空间中训练GAN，压缩关键特征
- 训练速度显著提升，资源消耗大幅降低
- 适合追求高效生成与突破性创意的研究者
生成对抗网络（GAN）在生成建模方面取得了显著进展，但其训练通常资源消耗大，需大量计算时间及数十万次迭代。本文提出一种新型优化方法，通过使用可逆映射（如自编码器）在初始数据的双空间中进行训练。在该双空间中，对数据的编码表示进行训练，这些表示包含数据中最显著的特征，使生成过程更加高效，并可能揭示超出人类认知的潜在模式。该方法不仅提升了训练速度和资源利用效率，还探讨了模型能否在人类生成数据的限制下，产生超越人类智能的生成洞察这一哲学问题。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GANs) have demonstrated remarkable advancements in generative modeling; however, their training is often resource-intensive, requiring extensive computational time and hundreds of thousands of epochs. This paper proposes a novel optimization approach that transforms the training process by operating within a dual space of the initial data using invertible mappings, specifically autoencoders. By training GANs on the encoded representations in the dual space, which encapsulate the most salient features of the data, the generative process becomes significantly more efficient and potentially reveals underlying patterns beyond human recognition. This approach not only enhances training speed and resource usage but also explores the philosophical question of whether models can generate insights that transcend the human intelligence while being limited by the human-generated data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。