The paper "Generating Diverse High-Fidelity Images with VQ-VAE-2" explores the use of Vector Quantized Variational AutoEncoders (VQ-VAE) for large-scale image generation. The authors scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples with higher coherence and fidelity. They use simple feed-forward encoder and decoder networks, making the model suitable for applications where encoding and decoding speed is critical. The VQ-VAE model only requires sampling in the compressed latent space, which is significantly faster than sampling in the pixel space, especially for large images. The proposed method involves a two-stage approach: first, a hierarchical VQ-VAE is trained to encode images into a discrete latent space, and then a powerful PixelCNN prior is learned over this latent space. The top-level prior models global information, while the bottom-level prior captures local details. This hierarchical structure allows for the encoding of complementary information at each level, reducing reconstruction errors. The authors demonstrate that their multi-scale hierarchical VQ-VAE, augmented with powerful priors over the latent codes, can generate samples of high quality that rival those of state-of-the-art Generative Adversarial Networks (GANs) on datasets like ImageNet, while avoiding GAN's known issues such as mode collapse and lack of diversity. They also propose an automated method for trading off diversity and quality of samples based on a classifier's ability to correctly classify the samples. Experiments show that the proposed method generates high-quality, sharp samples with broader diversity compared to GANs. The model is evaluated using metrics such as negative log-likelihood, precision-recall, classification accuracy score, and FID and Inception Scores, all of which demonstrate the effectiveness of the proposed method.The paper "Generating Diverse High-Fidelity Images with VQ-VAE-2" explores the use of Vector Quantized Variational AutoEncoders (VQ-VAE) for large-scale image generation. The authors scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples with higher coherence and fidelity. They use simple feed-forward encoder and decoder networks, making the model suitable for applications where encoding and decoding speed is critical. The VQ-VAE model only requires sampling in the compressed latent space, which is significantly faster than sampling in the pixel space, especially for large images. The proposed method involves a two-stage approach: first, a hierarchical VQ-VAE is trained to encode images into a discrete latent space, and then a powerful PixelCNN prior is learned over this latent space. The top-level prior models global information, while the bottom-level prior captures local details. This hierarchical structure allows for the encoding of complementary information at each level, reducing reconstruction errors. The authors demonstrate that their multi-scale hierarchical VQ-VAE, augmented with powerful priors over the latent codes, can generate samples of high quality that rival those of state-of-the-art Generative Adversarial Networks (GANs) on datasets like ImageNet, while avoiding GAN's known issues such as mode collapse and lack of diversity. They also propose an automated method for trading off diversity and quality of samples based on a classifier's ability to correctly classify the samples. Experiments show that the proposed method generates high-quality, sharp samples with broader diversity compared to GANs. The model is evaluated using metrics such as negative log-likelihood, precision-recall, classification accuracy score, and FID and Inception Scores, all of which demonstrate the effectiveness of the proposed method.