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Meta-Learning

348 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 )

Benchmarks

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.

Ranked #3 on 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

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

Meta-Learning Requires Meta-Augmentation

NeurIPS 2020 google-research/google-research

Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task.

META-LEARNING

Data Valuation using Reinforcement Learning

ICML 2020 google-research/google-research

To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).

DOMAIN ADAPTATION META-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

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

Deep Compressed Sensing

16 May 2019deepmind/deepmind-research

CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process.

META-LEARNING