Few-Shot Learning
1068 papers with code • 25 benchmarks • 42 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
GLUE
SST
CUB-200-2011
<h2>oi</h2>
UCF101
SST-2
Oxford 102 Flower
MRPC
DTD
Subtasks
Most implemented papers
The Power of Scale for Parameter-Efficient Prompt Tuning
google-research/prompt-tuning
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EMNLP 2021
More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned).
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
How to train your MAML
AntreasAntoniou/HowToTrainYourMAMLPytorch
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ICLR 2019
The field of few-shot learning has recently seen substantial advancements.
Making Pre-trained Language Models Better Few-shot Learners
princeton-nlp/LM-BFF
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ACL 2021
We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Compact Bilinear Pooling
gy20073/compact_bilinear_pooling
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CVPR 2016
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition.
Big Transfer (BiT): General Visual Representation Learning
google-research/big_transfer
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ECCV 2020
We conduct detailed analysis of the main components that lead to high transfer performance.
Data Augmentation Generative Adversarial Networks
AntreasAntoniou/DAGAN
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ICLR 2018
The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items.
Meta-Learning with Differentiable Convex Optimization
kjunelee/MetaOptNet
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CVPR 2019
We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
