MAML is a Noisy Contrastive Learner in Classification

29 June 2021

Papers citing "MAML is a Noisy Contrastive Learner in Classification"

10 / 10 papers shown

Title
Learn To Learn More Precisely Runxi Cheng Yongxian Wei Xianglong He Wanyun Zhu Songsong Huang Fei Richard Yu Fei Ma Chun Yuan 41 0 0 08 Aug 2024
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training Y. Liu Mengyu Li Ximing Li Lan Huang Fausto Giunchiglia Yanchun Liang Xiaoyue Feng Renchu Guan SSL 43 4 0 20 Jul 2024
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition H. Nghiem Umang Gupta Fred Morstatter 25 4 0 05 Feb 2024
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification Qianyu Guo Huifang Du Xing Jia Shuyong Gao Yan Teng Haofen Wang Wenqiang Zhang MedIm VLM 19 0 0 14 Oct 2023
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise Jeroen Galjaard Robert Birke Juan F. Pérez Lydia Y. Chen NoLa 19 0 0 12 Sep 2023
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning Wenwen Qiang Changwen Zheng Jingyao Wang Changwen Zheng SSL 17 1 0 28 Aug 2023
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks Liam Collins Hamed Hassani Mahdi Soltanolkotabi Aryan Mokhtari Sanjay Shakkottai 36 10 0 13 Jul 2023
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks Jun Shu Deyu Meng Zongben Xu 26 6 0 06 Jul 2021
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 177 639 0 19 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 338 11,684 0 09 Mar 2017