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이번에 리뷰할 논문은 Taming Transformers for High-Resolution Image Synthesis 입니다.https://arxiv.org/abs/2012.09841 Taming Transformers for High-Resolution Image SynthesisDesigned to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This..

이번에 리뷰할 논문은 Neural Discrete Representation Learning 입니다.https://arxiv.org/abs/1711.00937 Neural Discrete Representation LearningLearning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncarxiv.org     ..

이번에 리뷰할 논문은 SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation 입니다. https://paperswithcode.com/paper/samscore-a-semantic-structural-similarity/review/ Papers with Code - Paper tables with annotated results for SAMScore: A Semantic Structural Similarity Metric for Image Translati Paper tables with annotated results for SAMScore: A Semantic Structural Similarity Met..