Dual Contradistinctive Generative Autoencoder
Dual Contradistinctive Generative Autoencoder
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DOI：
10.1109/cvpr46437.2021.00088
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Gaurav Parmar;Dacheng Li;Kwonjoon Lee;Z. Tu
中科院分区：
文献类型：
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作者：
Gaurav Parmar;Dacheng Li;Kwonjoon Lee;Z. Tu
我们提出了一种新的生成式自动编码器模型，具有双对比度损失，以提高生成式自动编码器，同时进行推理（重建）和合成（采样）。我们的模型，命名为双对比生成自动编码器（DC-VAE），集成了实例级判别损失（保持重建/合成的实例级保真度）与集合级对抗损失（鼓励重建/合成的集合级保真度），两者都是对比的。报道了DC-VAE在32×32、64×64、128×128和512×512等不同分辨率下的广泛实验结果。在DC-VAE中，VAE中的两种截然不同的损失和谐地工作，导致在没有架构变化的情况下，基线VAE的显著的定性和定量性能增强。国家的最先进的或有竞争力的结果之间的生成自动编码器的图像重建，图像合成，图像插值和表示学习观察。DC-VAE是一种通用的VAE模型，适用于计算机视觉和机器学习中的各种下游任务。
We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for the reconstruction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions including 32×32, 64×64, 128×128, and 512×512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation, and representation learning are observed. DC-VAE is a general-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning.