2020/07/13 by Miaoyun Zhao, Yulai Cong, Zhao, Miaoyun +5 · 1 citation
Computer Science · Medicine · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2007.06178
openalex publication_date 2020/07/13 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learning encourages the capture of all data modes, and it is typically characterized by stable training. However, ML learning tends to distribute probability mass diffusely over the data space, e.g., yielding blurry synthetic images. Adversarial learning is well known to synthesize highly realistic natural images, despite practical challenges like mode dropping and delicate training. We propose an α-Bridge to unify the advantages of ML and adversarial learning, enabling the smooth transfer from one to the other via the α-divergence. We reveal that generalizations of the α-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the α-Bridge performs well in practice.