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Meta-Learning

152 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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Latest papers with code

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

ICLR 2020 qwerlanksdf/L2D

We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner.

DECISION MAKING META-LEARNING

0
01 Jan 2020

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

ICLR 2020 metabo-iclr2020/MetaBO

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the area of global optimization algorithms.

GAUSSIAN PROCESSES META-LEARNING TRANSFER LEARNING

0
01 Jan 2020

BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

3 Dec 2019GoodAI/badger-2019

In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication.

META-LEARNING

6
03 Dec 2019

Online-Within-Online Meta-Learning

NeurIPS 2019 dstamos/Adversarial-LTL

We study the problem of learning a series of tasks in a fully online Meta-Learning setting.

META-LEARNING

3
01 Dec 2019

Regularized Fine-grained Meta Face Anti-spoofing

25 Nov 2019rshaojimmy/AAAI2020-RFMetaFAS

Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration.

DOMAIN GENERALIZATION FACE ANTI-SPOOFING FACE RECOGNITION META-LEARNING

25
25 Nov 2019

Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

18 Nov 2019Howardqlz/Meta-MTL

In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task.

DATA AUGMENTATION IMAGE CLASSIFICATION META-LEARNING MULTI-TASK LEARNING OMNIGLOT

1
18 Nov 2019

Self-Supervised Learning For Few-Shot Image Classification

14 Nov 2019phecy/SSL-FEW-SHOT

In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide slow and robust representation for downstream tasks by learning from the data itself.

FEW-SHOT IMAGE CLASSIFICATION

18
14 Nov 2019

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

12 Nov 2019mileyan/simple_shot

Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING

29
12 Nov 2019

Learning to reinforcement learn for Neural Architecture Search

9 Nov 2019gomerudo/nas-env

We empirically investigate the agent's behavior during training when challenged to design chain-structured neural architectures for three datasets with increasing levels of hardness, to later fix the policy and evaluate it on two unseen datasets of different difficulty.

META-LEARNING NEURAL ARCHITECTURE SEARCH

1
09 Nov 2019