Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances

Gharoun, Hassan; Momenifar, Fereshteh; Chen, Fang; Gandomi, Amir H.

doi:10.1145/3659943

Cited by 13 publications

(1 citation statement)

References 77 publications

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“…Meta-learning [37][38][39], also known as "learning to learn", is a machine learning paradigm inspired by the human ability to acquire new concepts and skills quickly and efficiently. Generally, a good meta-learning model is expected to adapt or generalize well to new tasks and environments that have never been encountered during training [40]. The adaptation process, which occurs during testing, is essentially a mini-learning session with access to only a limited number of new task examples [41].…”

Section: Meta-learningmentioning

confidence: 99%

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

Zhang,

Xu,

Chen

et al. 2024

Remote Sensing

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Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) since their intrinsic non-linear feature extraction ability can enhance the separability between targets and the clutter. However, existing DL-based detectors are unattractive since they require a large amount of independent and identically distributed (i.i.d.) training samples of target tasks and fail to be generalized to the other new tasks. Given this issue, incorporating the strategy of meta-learning, we reformulate the RTD task as a few-shot classification problem and develop the Inter-frame Contrastive Learning-Based Meta Detector (IfCMD) to generalize to the new task efficiently with only a few samples. Moreover, to further separate targets from the clutter, we equip our model with Siamese architecture and introduce the supervised contrastive loss into the proposed model to explore hard negative samples, which have the targets overwhelmed by the clutter in the Doppler domain. Experimental results on simulated data demonstrate competitive detection performance for moving targets and superior generalization ability for new tasks of the proposed method.

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Section: Meta-learningmentioning

confidence: 99%

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

Zhang,

Xu,

Chen

et al. 2024

Remote Sensing

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MJP: A Meta-learning Approach for Chinese Legal Judgment Prediction

Lang,

Hou,

Chen

et al. 2024

Lecture Notes in Computer Science

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Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Rather,

Kumar,

Gandomi

2024

Artif Intell Rev

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Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

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Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances

Cited by 13 publications

References 77 publications

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

MJP: A Meta-learning Approach for Chinese Legal Judgment Prediction

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

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