Diffusion inversion is a task of recovering the noise of an image in a diffusion model, which is vital for controllable diffusion image editing. At present, diffusion inversion still remains a challenging task due to the lack of viable supervision signals. Thus, most existing methods resort to approximation-based solutions, which however are often at the cost of performance or efficiency. To remedy these shortcomings, we propose a novel self-supervised diffusion inversion approach in this paper, termed Deep Inversion (DeepInv). Instead of requiring ground-truth noise annotations, we introduce a self-supervised objective as well as a data augmentation strategy to generate high-quality pseudo noises from real images without manual intervention. Based on these two innovative designs, DeepInv is also equipped with an iterative and multi-scale training regime to train a parameterized inversion solver, thereby achieving the fast and accurate image-to-noise mapping. To the best of our knowledge, this is the first attempt of presenting a trainable solver to predict inversion noise step by step. The extensive experiments show that our DeepInv can achieve much better performance and inference speed than the compared methods, e.g., +40.435% SSIM than EasyInv and +9887.5% speed than ReNoise on COCO dataset. Moreover, our careful designs of trainable solvers can also provide insights to the community. Codes and model parameters will be released in https://github.com/potato-kitty/DeepInv.
GAN Inversion for Image Editing via Unsupervised Domain Adaptation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Virginia（弗吉尼亚大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Textual Inversion and Self-supervised Refinement for Radiology Report Generation
Sichuan University（四川大学）；Peking University（北京大学）；College of Computer Science, Sichuan University（四川大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；China Agricultural University（中国农业大学）；The Sixth People’s Hospital of Chengdu（成都市第六人民医院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）
InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training
University of Electronic Science and Technology of China（电子科技大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province（四川省脑机接口与脑启发智能重点实验室）
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion
College of computer science and technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）；Department of Automation, University of Science and Technology of China（中国科学技术大学自动化系）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）
High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting
University of California, Berkeley（加州大学伯克利分校）；Philips Research Europe（飞利浦欧洲研究院）；UC Berkeley-UCSF（加州大学伯克利分校-加州大学旧金山分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；International Computer Science Institute（国际计算机科学研究所）；University of Michigan（密歇根大学）
An Efficient Self-supervised Seismic Data Reconstruction Method Based on Self-Consistency Learning
The Key Laboratory of Earth Exploration & Information Techniques of Ministry Education（教育部地球探测与信息处理重点实验室）；School of Geophysics, Chengdu University of Technology（成都理工大学地球物理学院）；Chengdu University of Technology（成都理工大学）
Recovering Cloud Microstructures with Cascaded Diffusion Inversion
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Hebrew University of Jerusalem（耶路撒冷希伯来大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
