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Meta-Learning

258 papers with code · Methodology

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

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Greatest papers with code

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

NeurIPS 2018 tensorflow/models

Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.

#3 best model for Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)

IMAGE CLASSIFICATION META-LEARNING SEMANTIC SEGMENTATION STREET SCENE PARSING

Meta-Learning Update Rules for Unsupervised Representation Learning

ICLR 2019 tensorflow/models

Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.

META-LEARNING UNSUPERVISED REPRESENTATION LEARNING

NoRML: No-Reward Meta Learning

4 Mar 2019google-research/google-research

To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).

META-LEARNING

Meta-Learning without Memorization

ICLR 2020 google-research/google-research

If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.

FEW-SHOT IMAGE CLASSIFICATION

Learning What and Where to Transfer

15 May 2019jindongwang/transferlearning

To address the issue, we propose a novel transfer learning approach based on meta-learning that can automatically learn what knowledge to transfer from the source network to where in the target network.

META-LEARNING SMALL DATA IMAGE CLASSIFICATION TRANSFER LEARNING

Auto-Sklearn 2.0: The Next Generation

8 Jul 2020automl/auto-sklearn

In this paper we introduce new Automated Machine Learning (AutoML) techniques motivated by our winning submission to the second ChaLearn AutoML challenge, PoSH Auto-sklearn.

AUTOML META-LEARNING

Learning to learn by gradient descent by gradient descent

NeurIPS 2016 deepmind/learning-to-learn

The move from hand-designed features to learned features in machine learning has been wildly successful.

META-LEARNING

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

ICML 2017 cbfinn/maml

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT REGRESSION ONE-SHOT LEARNING

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

6 Mar 2020tensorflow/quantum

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.

META-LEARNING QUANTUM APPROXIMATE OPTIMIZATION

Torchmeta: A Meta-Learning library for PyTorch

14 Sep 2019tristandeleu/pytorch-meta

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research.

META-LEARNING