Summary of Self-supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods, by Daniel Otero et al.
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methodsby Daniel Otero, Rafael Mateus, Randall…
Summary of Enhancing Graph Self-supervised Learning with Graph Interplay, by Xinjian Zhao et al.
Enhancing Graph Self-Supervised Learning with Graph Interplayby Xinjian Zhao, Wei Pang, Xiangru Jian, Yaoyao Xu,…
Summary of Improving Node Representation by Boosting Target-aware Contrastive Loss, By Ying-chun Lin et al.
Improving Node Representation by Boosting Target-Aware Contrastive Lossby Ying-Chun Lin, Jennifer NevilleFirst submitted to arxiv…
Summary of Denoising with a Joint-embedding Predictive Architecture, by Dengsheng Chen et al.
Denoising with a Joint-Embedding Predictive Architectureby Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua WuFirst submitted…
Summary of Cudle: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments, by Reza Rahimi Azghan et al.
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environmentsby Reza Rahimi Azghan,…
Summary of Bissl: a Bilevel Optimization Framework For Enhancing the Alignment Between Self-supervised Pre-training and Downstream Fine-tuning, by Gustav Wagner Zakarias et al.
BiSSL: A Bilevel Optimization Framework for Enhancing the Alignment Between Self-Supervised Pre-Training and Downstream Fine-Tuningby…
Summary of Dataset Distillation Via Knowledge Distillation: Towards Efficient Self-supervised Pre-training Of Deep Networks, by Siddharth Joshi et al.
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networksby Siddharth Joshi, Jiayi…
Summary of Taegan: Generating Synthetic Tabular Data For Data Augmentation, by Jiayu Li et al.
TAEGAN: Generating Synthetic Tabular Data For Data Augmentationby Jiayu Li, Zilong Zhao, Kevin Yee, Uzair…
Summary of Meta-ttt: a Meta-learning Minimax Framework For Test-time Training, by Chen Tao et al.
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Trainingby Chen Tao, Li Shen, Soumik MondalFirst submitted…
Summary of Contrastive Abstraction For Reinforcement Learning, by Vihang Patil et al.
Contrastive Abstraction for Reinforcement Learningby Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer, Sepp HochreiterFirst submitted to…