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 there construction/synthesis), both being
contradistinctive. Extensive experimental results by DC-VAE across different
resolutions including 32x32, 64x64, 128x128, and 512x512 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.

我们提出了一个名为 DC-VAE 的旨在改善生成自动编码器的新模型，其具有双重对立损失，可以同时执行推理和合成操作。该模型通过将单元级区分性损失和集合级对抗性损失相结合，实现了单元级还原 / 合成的精度和集合级准确度的提高，为计算机视觉和机器学习中的各种下游任务提供了广泛的适用性。