mlsanityData to 7 Oct 2026

Synthesizing Photorealistic Images with Deep Generative Learning

arXiv 2202.12752 ↗First posted 23 Feb 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and completed scene decomposition. This thesis consists of five pieces of work, each of which presents a new learning-based approach for synthesizing images with plausible content as well as visually realistic appearance. Each work demonstrates the superiority of the proposed approach on image synthesis, with some further contributing to other tasks, such as depth estimation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 237 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

28 of 237 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  7. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  8. 2014Conditional Generative Adversarial Netscited by 2,152
  9. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  10. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  11. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  12. 2016Least Squares Generative Adversarial Networkscited by 1,000
16 more
  1. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  2. 2020Pre-Trained Image Processing Transformercited by 891
  3. 2019MONet: Unsupervised Scene Decomposition and Representationcited by 388
  4. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 375
  5. 2020Visual Transformers: Token-based Image Representation and Processing for Computer Visioncited by 333
  6. 2015Deep multi-scale video prediction beyond mean square errorcited by 297
  7. 2021TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Upcited by 162
  8. 2021High-Fidelity Pluralistic Image Completion with Transformerscited by 134
  9. 2021Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAEcited by 106
  10. 2019SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Colorcited by 105
  11. 2018SPG-Net: Segmentation Prediction and Guidance Network for Image Inpaintingcited by 58
  12. 2020Rethinking the Truly Unsupervised Image-to-Image Translationcited by 56
  13. 2017Variational Approaches for Auto-Encoding Generative Adversarial Networkscited by 52
  14. 2020Spherical Image Generation from a Single Normal Field of View Image by Considering Scene Symmetrycited by 5
  15. 2018High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networkscited by 3
  16. 2020CR-Fill: Generative Image Inpainting with Auxiliary Contexutal Reconstructioncited by 2

Built on by

Not cited yet.