Few-Shot Learning
1036 papers with code • 22 benchmarks • 41 datasets
Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
Benchmarks
These leaderboards are used to track progress in Few-Shot Learning
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Libraries
Use these libraries to find Few-Shot Learning models and implementations
ImageNet
GLUE
SST
CUB-200-2011
UCF101
SST-2
Oxford 102 Flower
MRPC
DTD
Subtasks
Latest papers with no code
Few-Shot Object Detection: Research Advances and Challenges
This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions.
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
Extensive empirical studies are conducted in terms of validating the effectiveness of NMF, especially its supervised variants (e. g., discriminative NMF, and supervised and constrained NMF with sparseness), and the comparison with principal component analysis (PCA), i. e., the collaborative representation-based dimensionality reduction technique derived from eigenvectors.
SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition
Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions.
Class-Incremental Few-Shot Event Detection
Therefore, this paper proposes a new task, called class-incremental few-shot event detection.
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies.
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering
To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information.
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark
The dataset includes high-quality and densely captured room impulse response data paired with multi-view images, and precise 6DoF pose tracking data for sound emitters and listeners in the rooms.
Few-Shot Cross-System Anomaly Trace Classification for Microservice-based systems
Within the same MSS, our framework achieves an average accuracy of 93. 26\% and 85. 2\% across 50 meta-testing tasks for Trainticket and OnlineBoutique, respectively, when provided with 10 instances for each task.
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization
This paper introduces a novel approach to temporal action localization (TAL) in few-shot learning.
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.
