通过监控生成模型训练过程，提前发现并纠正偏差。
Progressive Monitoring of Generative Model Training Evolution
- 用降维技术分析隐空间和数据分布演化
- 可及时发现训练异常并干预以提升生成质量
- 适合关注生成模型训练稳定性与效率的研究者
尽管深度生成模型（DGMs）广受欢迎，其易受偏见及其他低效问题影响，导致不良结果的问题依然存在。随着模型复杂度上升，亟需在早期识别潜在问题以获得理想结果并优化资源。为此，我们提出一种渐进式分析框架，用于监控 DGMs 的训练过程。该方法利用降维技术，便于观察隐表示、生成分布与真实分布及其在训练迭代中的演化。若发现表示或分布演化异常，可及时暂停并调整训练策略。此方法有助于分析模型训练动态，实现对偏见与失败的及时识别，减少计算开销。我们展示了该方法如何在生成对抗网络（GAN）训练中早期识别并缓解偏见，从而改善生成数据分布的质量。
原文摘要 · Abstract (English)
While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue. With their growing complexity, there is a critical need for early detection of issues to achieve desired results and optimize resources. Hence, we introduce a progressive analysis framework to monitor the training process of DGMs. Our method utilizes dimensionality reduction techniques to facilitate the inspection of latent representations, the generated and real distributions, and their evolution across training iterations. This monitoring allows us to pause and fix the training method if the representations or distributions progress undesirably. This approach allows for the analysis of a models' training dynamics and the timely identification of biases and failures, minimizing computational loads. We demonstrate how our method supports identifying and mitigating biases early in training a Generative Adversarial Network (GAN) and improving the quality of the generated data distribution.
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。