ContraGAN: Contrastive Learning for Conditional Image Generation

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Towards Language-Free Training for Text-to-Image Generation (2021, neutral): “Similar ideas have been exploited in [15, 17, 56].”
Training GANs with Stronger Augmentations via Contrastive Discriminator (2021, background): “For conditional GANs, Kang & Park (2020) have proposed a contrastive form of loss to efﬁciently incorporate a given conditional information into discriminators.”
Dual Contrastive Loss and Attention for GANs (2021, background): “The progress is mainly driven by large-scale datasets [18, 57, 91, 38, 54, 42], architectural tuning [10, 98, 42, 43, 69], and loss designs [58, 3, 25, 60, 39, 101, 105, 96, 40, 106, 36].”
Domain-Independent Dominance of Adaptive Methods (2019, background): “Note that the performance achieved by Adam matches other sources(3) [20], and Adam performed best with α = 0.”
Data-Efficient Instance Generation from Instance Discrimination (2021, background): “Akin to supervised contrastive loss [27], some concurrent work [20, 21, 43] reformulates the conventional bi-classification task (i.”
Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks (2021, methodology): “All experiments were based on the popular PyTorch-StudioGAN repo [111].”