Self Supervised
Self-supervised learning (SSL) aims to train machine learning models using unlabeled data by designing pretext tasks that encourage the model to learn useful representations. Current research focuses on improving generalization, mitigating overfitting, and developing efficient architectures like transformers and CNNs for various modalities (images, audio, point clouds, fMRI data). SSL's significance lies in its ability to leverage vast amounts of readily available unlabeled data, leading to improved performance on downstream tasks and reducing the reliance on expensive and time-consuming manual labeling, particularly impacting fields like medical imaging, speech processing, and autonomous driving.
Papers
Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection Under Domain Shift
Haiyan Lan, Qiaoxi Zhu, Jian Guan, Yuming Wei, Wenwu Wang
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
Soumi Maiti, Yifan Peng, Shukjae Choi, Jee-weon Jung, Xuankai Chang, Shinji Watanabe
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Atito, Muhammad Awais, Zhenhua Feng
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
Yunyong Ko, Hanghang Tong, Sang-Wook Kim
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Titouan Parcollet, Ha Nguyen, Solene Evain, Marcely Zanon Boito, Adrien Pupier, Salima Mdhaffar, Hang Le, Sina Alisamir, Natalia Tomashenko, Marco Dinarelli, Shucong Zhang, Alexandre Allauzen, Maximin Coavoux, Yannick Esteve, Mickael Rouvier, Jerome Goulian, Benjamin Lecouteux, Francois Portet, Solange Rossato, Fabien Ringeval, Didier Schwab, Laurent Besacier
DeCUR: decoupling common & unique representations for multimodal self-supervision
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Chenying Liu, Zhitong Xiong, Xiao Xiang Zhu
Adapting Self-Supervised Representations to Multi-Domain Setups
Neha Kalibhat, Sam Sharpe, Jeremy Goodsitt, Bayan Bruss, Soheil Feizi
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
Yusuf Brima, Ulf Krumnack, Simone Pika, Gunther Heidemann
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
Zhengyang Lu, Ying Chen
Variational Self-Supervised Contrastive Learning Using Beta Divergence
Mehmet Can Yavuz, Berrin Yanikoglu
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective
Alain Riou, Stefan Lattner, Gaëtan Hadjeres, Geoffroy Peeters
Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data
Mariona Carós, Ariadna Just, Santi Seguí, Jordi Vitrià
Self-supervised Semantic Segmentation: Consistency over Transformation
Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Yury Velichko, Ulas Bagci, Dorit Merhof
CL-MAE: Curriculum-Learned Masked Autoencoders
Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi, Thomas B. Moeslund, Radu Tudor Ionescu