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.
翻译:我们展示了一种新的基因化自动编码模型,具有双重反向损失,以改善同时进行推断(重建)和合成(抽样)的基因自动编码元件。我们的模型称为双反异基因自动编码元件(DC-VAE),将实例一级的歧视性损失(保持对重建/合成的试想水平的忠诚)与既定水平的对抗性损失(鼓励当地建筑/合成的定值忠诚)结合起来,两者都是相反的。DC-VAE在不同决议中的广泛实验结果,包括32x32、64x64、128x128和512x512。在DC-VAE中,VAE工作的两种异常损失和谐地使基线VAE在质量和数量上显著提高,而没有建筑变化。在图像重建、图像合成、图像合成、图像交叉和代表性学习方面,都观察到了国家-艺术或竞争的结果。DC-VAE是通用计算机模型的一个通用和下游版本。