mmrl/lost-in-latent-space
Code from the article: "Lost in Latent Space: Examining Failures of Disentangled Models at Combinatorial Generalisaton" (NeurIPS, 2022)
This project helps researchers and machine learning practitioners understand why generative AI models, specifically those designed to 'disentangle' different properties of an object (like its shape, color, or position), struggle to create realistic new combinations of those properties. It takes in datasets of images with varied features and outputs analyses of how well different model architectures handle creating novel combinations. This is for anyone researching or implementing advanced generative models and their ability to generalize.
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Use this if you are developing or evaluating disentangled generative models and need to rigorously test their ability to produce novel combinations of features.
Not ideal if you are looking for a pre-trained model to immediately generate high-quality images or for applications not related to evaluating model generalization.
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Python
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Last pushed
Jan 04, 2023
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