Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

13 January 2022

Papers citing "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning"

9 / 9 papers shown

Title
Robust Meta-Representation Learning via Global Label Inference and Classification Ruohan Wang Isak Falk Massimiliano Pontil C. Ciliberto 36 3 0 22 Dec 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen Sijia Liu Shiyu Chang Lisa Amini Zhangyang Wang CLL 26 4 0 15 Jun 2022
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning Yang Shu Zhangjie Cao Jing Gao Jianmin Wang Philip S. Yu Mingsheng Long 32 10 0 14 Oct 2021
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation Adam Scibior Vasileios Lioutas Daniele Reda Peyman Bateni Frank D. Wood VGen 56 47 0 22 Apr 2021
Free Lunch for Few-shot Learning: Distribution Calibration Shuo Yang Lu Liu Min Xu OODD 219 322 0 16 Jan 2021
CrossTransformers: spatially-aware few-shot transfer Carl Doersch Ankush Gupta Andrew Zisserman ViT 215 330 0 22 Jul 2020
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning Yinbo Chen Zhuang Liu Huijuan Xu Trevor Darrell Xiaolong Wang 175 342 0 09 Mar 2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning J. Bronskill Jonathan Gordon James Requeima Sebastian Nowozin Richard Turner 71 89 0 06 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 341 11,684 0 09 Mar 2017