06-16-2022, 09:04 AM
For a pair of positive multi-modal samples, contrastive learning framework attempts to minimize the distance between the different modalities while pushing away the unrelated samples. The pre-trained model could be used for comparing the representation of image, text and audio. Works that optimize image generating process generally minimize the distance between the generated image representation and text prompt representation. However, CNN models fail to encode the long-range interaction between local features. The discretized code book enables transformer model to compute attention between the local features. Using the encoder network that maps the original image into the latent space, VQ-GAN additionally uses the vector quantization process to represent the latent space using code book. Using the discretized latent space, generator reconstructs the latent code into realistic images using adversarial loss between generator and discriminator. Linear manipulation in the latent space of GAN corresponds to a different generated image. The extension of CLIP which is originally a visual-language model allows audio-based image generation.
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