Progressive Monitoring of Generative Model Training Evolution

• URL: http://arxiv.org/abs/2412.12755v1
• Date: Tue, 17 Dec 2024 10:20:29 GMT
• Title: Progressive Monitoring of Generative Model Training Evolution
• Authors: Vidya Prasad, Anna Vilanova, Nicola Pezzotti,
• Abstract summary: Deep generative models (DGMs) have gained popularity, but their susceptibility to biases and other inefficiencies remains an issue. We introduce a progressive analysis framework to monitor the training process of DGMs. We demonstrate how our method supports identifying and mitigating biases early in training a Generative Adversarial Network (GAN)
• Score: 1.3108652488669736
• License:
• Abstract: 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.

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