VQ-GAN: Taming Transformers for High-Resolution Image Synthesis | Paper Explained

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In this video I cover VQ-GAN or Taming Transformers for High-Resolution Image Synthesis.

It uses modified VQ-VAEs and a powerful transformer (GPT-2) to synthesize high-res images.

An important modification of VQ-VAE they brought are:
1) changing MSE for perceptual loss
2) adding adversarial loss which makes the images way more crispy compared to the original VQ-VAE which had blurry outputs.

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✅ Paper: https://arxiv.org/abs/2012.09841
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⌚️ Timetable:
00:00 Intro
01:50 A high-level VQ-GAN overview
04:00 Perceptual loss
05:10 Patch-based adversarial loss
06:45 Sequence prediction via GPT
09:50 Generating high-res images
12:45 Loss explained in depth
16:15 Training the transformer
17:50 Conditioning transformer
20:45 Comparisons and results
22:00 Sampling strategies
23:00 Comparisons and results continued
25:00 Rejection sampling with ResNet or CLIP
26:45 Receptive field effects
28:30 Comparisons with DALL-E

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#vqvae #imagesynthesis #gpt