schwettmann/visual-vocab
Pytorch-based tools for constructing a vocabulary of visual concepts in a GAN.
This tool helps researchers and vision scientists understand what visual concepts a Generative Adversarial Network (GAN) has learned. You input a pre-trained GAN and optionally your own image annotations, and it outputs a 'vocabulary' of human-interpretable visual concepts (like 'striped' or 'sky'). This is for anyone studying how AI models perceive and represent the visual world.
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Use this if you want to extract and visualize the specific visual features or ideas a GAN has learned, expressed in plain language.
Not ideal if you're looking to train new image generation models from scratch or simply use a GAN for image synthesis without analyzing its internal representations.
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MIT
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Last pushed
Feb 25, 2022
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