Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis

By Yong Guo, Qi Chen, Qingyao Wu, Javen Qinfeng Shi, Mingkui Tan. 2019

69 citations recorded by the source corpus.

Generating images via a generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called auto-embedding generative adversarial network, which simultaneously encodes the global structure features and captures the fine-grained details. In our network, we use an autoencoder to learn the intrinsic high-level structure of real images and design a novel denoiser network to provide photo-realistic details for the generated images. In the experiments, we are able to produce $512 \times 512$ images of promising quality directly from the input noise. The resultant images exhibit better perceptual photo-realism, that is, with sharper structure and richer details, than other baselines on several datasets, including Oxford-102 Flowers, Caltech-UCSD Birds (CUB), High-Quality Large-scale CelebFaces Attributes (CelebA-HQ), Large-scale Scene Understanding (LSUN), and ImageNet.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mingkui Tan
2. Jian Chen
3. Yong Guo
4. Peilin Zhao
5. Junzhou Huang
6. Chuang Gan
7. Qi Chen
8. Qing Du

Citations in the corpus

• Hierarchical Neural Architecture Search for Single Image Super-Resolution (2020, methodology): “layers (e.g., SRCNN [6] and LapSRN [7]) and the cell-level architecture controls the computation of each block/layer (e.g., RCAB [8]). In practice, designing deep models is often very labor-intensive [9]–[12] and the handcrafted architectures are often not optimal in practice. Regarding this issue, ”
• Location-Aware Graph Convolutional Networks for Video Question Answering (2020, background): “Recently, deep learning has witnessed a great process (Tan, Tsang, and Wang 2014; Cao et al. 2018; 2019; Gan et al. 2019; Guo et al. 2019a).”
• Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (2021, methodology): “Although our self-supervision strategy for D comes in the form of an auto-encoder (AE), this approach is fundamentally different from works trying to combine GAN and AE (Larsen et al., 2016; Guo et al., 2019; Zhao et al., 2016; Berthelot et al., 2017).”
• Discrimination-Aware Network Pruning for Deep Model Compression (2020, background): “edu image generation [5], [13], [15], video analysis [63], [76], [83] and many other areas [14], [20].”
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019, background): “…networks (DNNs) [25] have been producing state-of-the-art results in many challenging tasks including image classification [12, 23, 43, 60, 61, 18, 11, 56], face recognition [39, 44, 59], brain signal processing [34, 35], video analysis [48, 53, 52] and many other areas [58, 57, 24, 3, 10, 9, 4].”
• Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution (2020, unknown): “44, 6] and many other applications [7, 50, 52, 11, 20].”
• Graph Convolutional Networks for Temporal Action Localization (2019, background): “Recently, great progress has been achieved in deep learning [5, 9, 19, 53], which facilitates the development of temporal action localization.”
• Dense Regression Network for Video Grounding (2020, background): “Recently, great progress has been achieved in deep learning [48, 47, 2, 15, 13, 14, 19, 1, 51], which facilitates the development of video grounding.”