Defining Benchmarks for Continual Few-Shot Learning

2020-04-15 · Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, Amos Storkey

Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a suite of benchmarks for the highly desirable setting of continual few-shot learning, where the learner is presented a number of few-shot tasks, one after the other, and then asked to perform well on a validation set stemming from all previously seen tasks. Continual few-shot learning has a small computational footprint and is thus an excellent setting for efficient investigation and experimentation. In this paper we first define a theoretical framework for continual few-shot learning, taking into account recent literature, then we propose a range of flexible benchmarks that unify the evaluation criteria and allows exploring the problem from multiple perspectives. As part of the benchmark, we introduce a compact variant of ImageNet, called SlimageNet64, which retains all original 1000 classes but only contains 200 instances of each one (a total of 200K data-points) downscaled to 64 x 64 pixels. We provide baselines for the proposed benchmarks using a number of popular few-shot learning algorithms, as a result, exposing previously unknown strengths and weaknesses of those algorithms in continual and data-limited settings.

📄 PDF Abstract BibTeX arXiv:2004.11967

Code (2)

AntreasAntoniou/FewShotContinualLearning 공식 구현 pytorch

AntreasAntoniou/FewShotContinualLearningDataProvider pytorch

Tasks

continual few-shot learningContinual Learning Few-Shot Learning

Few-shot Continual Learning: a Brain-inspired Approach

2021-04-19 · Liyuan Wang, Qian Li, Yi Zhong, Jun Zhu

It is an important yet challenging setting to continually learn new tasks from a few examples. Although numerous efforts have been devoted to either continual learning or few-shot learning, little work has considered thi…

Continual Learning Few-Shot Learning

Continual Learning Improves Zero-Shot Action Recognition

2024-10-14 · Shreyank N Gowda, Davide Moltisanti, Laura Sevilla-Lara

Zero-shot action recognition requires a strong ability to generalize from pre-training and seen classes to novel unseen classes. Similarly, continual learning aims to develop models that can generalize effectively and le…

Action Recognition Continual Learning Zero-Shot Action Recognition Zero-Shot Learning

CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents

2021-10-19 · Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi 외

Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Co…

NetHack reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Block Contextual MDPs for Continual Learning

2021-10-13 · Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang

In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationary. This assumption of stationarity, while simplifying, can be unrealistic in ma…

Continual Learning Generalization Bounds reinforcement-learning Reinforcement Learning+1

Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models

2025-10-24 · Yujin Jo, Taesup Kim arxiv

Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated remarkable zero-shot generalization, enabling deployment in a wide range of real-world tasks without additional task-specific training. However, …

Zero-shot Generalization Continual Learning