Towards Few-Shot Learning in the Open World: A Review and Beyond
- URL: http://arxiv.org/abs/2408.09722v1
- Date: Mon, 19 Aug 2024 06:23:21 GMT
- Title: Towards Few-Shot Learning in the Open World: A Review and Beyond
- Authors: Hui Xue, Yuexuan An, Yongchun Qin, Wenqian Li, Yixin Wu, Yongjuan Che, Pengfei Fang, Minling Zhang,
- Abstract summary: Few-shot learning aims to mimic human intelligence by enabling significant generalizations and transferability.
This paper presents a review of recent advancements designed to adapt FSL for use in open-world settings.
We categorize existing methods into three distinct types of open-world few-shot learning: those involving varying instances, varying classes, and varying distributions.
- Score: 52.41344813375177
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, underpinned by prior knowledge. Few-shot learning (FSL) aims to mimic this capacity by enabling significant generalizations and transferability. However, traditional FSL frameworks often rely on assumptions of clean, complete, and static data, conditions that are seldom met in real-world environments. Such assumptions falter in the inherently uncertain, incomplete, and dynamic contexts of the open world. This paper presents a comprehensive review of recent advancements designed to adapt FSL for use in open-world settings. We categorize existing methods into three distinct types of open-world few-shot learning: those involving varying instances, varying classes, and varying distributions. Each category is discussed in terms of its specific challenges and methods, as well as its strengths and weaknesses. We standardize experimental settings and metric benchmarks across scenarios, and provide a comparative analysis of the performance of various methods. In conclusion, we outline potential future research directions for this evolving field. It is our hope that this review will catalyze further development of effective solutions to these complex challenges, thereby advancing the field of artificial intelligence.
Related papers
- A Unified and General Framework for Continual Learning [58.72671755989431]
Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge.
Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques.
This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
arXiv Detail & Related papers (2024-03-20T02:21:44Z) - Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm.
It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
arXiv Detail & Related papers (2024-03-04T06:25:26Z) - BOWLL: A Deceptively Simple Open World Lifelong Learner [22.375833943808995]
We propose a deceptively simple yet highly effective way to repurpose standard models for open world lifelong learning.
Our approach should serve as a future standard for models that are able to effectively maintain their knowledge, selectively focus on informative data, and accelerate future learning.
arXiv Detail & Related papers (2024-02-07T13:04:35Z) - A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication.
This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches.
We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
arXiv Detail & Related papers (2024-01-02T16:54:58Z) - Detecting and Learning Out-of-Distribution Data in the Open world:
Algorithm and Theory [15.875140867859209]
This thesis makes contributions to the realm of machine learning, specifically in the context of open-world scenarios.
Research investigates two intertwined steps essential for open-world machine learning: Out-of-distribution (OOD) Detection and Open-world Representation Learning (ORL)
arXiv Detail & Related papers (2023-10-10T00:25:21Z) - Open Environment Machine Learning [84.90891046882213]
Conventional machine learning studies assume close world scenarios where important factors of the learning process hold invariant.
This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions, varied learning objectives, and discusses some theoretical issues.
arXiv Detail & Related papers (2022-06-01T11:57:56Z) - A Comprehensive Survey of Few-shot Learning: Evolution, Applications,
Challenges, and Opportunities [5.809416101410813]
Few-shot learning has emerged as an effective learning method and shows great potential.
We extensively investigated 200+ latest papers on FSL published in the past three years.
We propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge.
arXiv Detail & Related papers (2022-05-13T16:24:35Z) - Bayesian Embeddings for Few-Shot Open World Recognition [60.39866770427436]
We extend embedding-based few-shot learning algorithms to the open-world recognition setting.
We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets.
arXiv Detail & Related papers (2021-07-29T00:38:47Z) - A Review of Open-World Learning and Steps Toward Open-World Learning
Without Labels [11.380522815465984]
In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data.
This paper formalizes various open-world learning problems including open-world learning without labels.
arXiv Detail & Related papers (2020-11-25T17:41:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.