A Concise Review of Recent Few-shot Meta-learning Methods
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Meta-LearningSimilar Papers 제목 키워드 기반
LLMs as Meta-Reviewers' Assistants: A Case Study
One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peers, formulating one's self-judgment as a …
A Comprehensive Review of Few-shot Action Recognition
Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data in action recognition. It requires accurately classifying human actions in videos using on…
Action RecognitionFew-Shot action recognitionFew Shot Action RecognitionFew-Shot Learning+6Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the meta-learning process where episodic tasks …
DecoderFew-Shot Image ClassificationGeneral Classificationimage-classification+2OrderSum: Reading Order-Aware Unsupervised Opinion Summarization
Opinion summarization aims to create a concise summary reflecting subjective information conveyed by multiple user reviews about the same product. To avoid the high expense of curating golden summaries for training, many…
Opinion SummarizationUnsupervised Opinion SummarizationImproving Few-Shot Learning through Multi-task Representation Learning Theory
In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start b…
Continual LearningFew-Shot LearningLearning TheoryMeta-Learning+1