Help Needed with "Mode Collapse" Error in Generative AI

Hello everyone,

Recently I started generative ai through generative ai course. now, I’ve been experimenting with generative AI models for creating synthetic data, but I keep encountering an error known as “Mode Collapse.” Despite trying different approaches and tweaking hyperparameters, I’m struggling to mitigate this issue effectively.

Error Name: Mode Collapse

Error Description: Mode collapse in generative adversarial networks (GANs) occurs when the generator learns to produce limited or repetitive samples, failing to capture the full diversity of the training data distribution. This results in a loss of variability and richness in the generated outputs, hindering the model’s ability to produce high-quality and diverse samples. Mode collapse can occur due to various factors such as imbalanced data, unstable training dynamics, or inadequate network architectures. Addressing mode collapse is crucial for ensuring the effectiveness and diversity of generated outputs in generative AI applications.

Thanks in advance!

What is the problem? There is already a section on GANs which deal with mode collapse, did you go though it?

It sounds like you are using some higher level package that must have GANs primitives as APIs, if it can explicitly report “Mode Collapse” as an error message. I have not explored what is out there today in terms of Generative AI platforms, but have taken the GANs Specialization here from DeepLearning.AI. It is excellent and highly recommended. As Gent says, they do address the issue of Mode Collapse at several points in the 3 courses of GANs. One particular place that I remember is in C1 W3, which is devoted to Wasserstein Loss and one of the primary motivations for using that approach is that it mitigates problems with Mode Collapse. Have you seen any material on that approach?

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Thanks for the solution. https://community.deeplearning.ai/t/help-needed-with-mode-collapse-error-in-generative-ai/574197generative ai

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Thanks for sharing these insights mate as I found it very much useful and informative.