Robust Contrastive Learning With Dynamic Mixed Margin

Abstract

One of the promising ways for the representation learning is contrastive learning. It enforces that positive pairs become close while negative pairs become far. Contrastive learning utilizes the relative proximity or distance between positive and negative pairs. However, contrastive learning might fail to handle the easily distinguished positive-negative pairs because the gradient of easily divided positive-negative pairs comes to vanish. To overcome the problem, we propose a dynamic mixed margin (DMM) loss that generates the augmented hard positive-negative pairs that are not easily clarified. DMM generates hard positive-negative pairs by interpolating the dataset with Mixup. Besides, DMM adopts the dynamic margin incorporating the interpolation ratio, and dynamic adaptation improves representation learning. DMM encourages making close for positive pairs far away, whereas making a little far for strongly nearby positive pairs alleviates overfitting. Our proposed DMM is a plug-and-play module compatible with diverse contrastive learning loss and metric learning. We validate that the DMM is superior to other baselines on various tasks, video-text retrieval, and recommender system task in unimodal and multimodal settings. Besides, representation learned from DMM shows better robustness even if the modality missing occurs that frequently appears on the real-world dataset. Implementation of DMM at downstream tasks is available here: https://github.com/teang1995/DMM INDEX TERMS Multimodal learning, contrastive learning, retrieval, video representation, recommender system, robustness. YONGTAEK LIM received the B.S. degree in electrical and computer engineering from Ajou University, in 2021. He is currently pursuing the M.S. degree in artificial intelligence with the University of Seoul. YEWON KIM received the B.S. degree in electrical and computer engineering from the University of Seoul, in 2022, where she is currently pursuing the M.S. degree in artificial intelligence. CHANGDAE OH received the B.S. degree in statistics from the University of Seoul, in 2022, where he is currently pursuing the M.S. degree in artificial intelligence. KYUNGWOO SONG received the B.S. degree in mathematical sciences and industrial and systems engineering and the M.S. and Ph.D. degrees in industrial and systems engineering from the Korea Advanced Institute of Science and Technology (KAIST), in 2017 and 2021.