Few-Shot Learning
1035 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
The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
mark-sky/kcl
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Few-shot learning aims to further enhance the transfer capability of CLIP by giving few images in each class, aka 'few shots'.
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time.
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
To this end, we disassemble three key components involved in computation of logit bias (i. e., logit features, logit predictor, and logit fusion) and empirically analyze the effect on performance of few-shot classification.
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies
With the surge of ChatGPT, the use of large models has significantly increased, rapidly rising to prominence across the industry and sweeping across the internet.
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models.
PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
3DAgentWorld/Toolkit-for-Prompt-Compression
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Prompt compression is an innovative method for efficiently condensing input prompts while preserving essential information.
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
zshanggu/cdmm
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Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features.
MatchSeg: Towards Better Segmentation via Reference Image Matching
keeplearning-again/matchseg
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Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
