Browse SoTA > Methodology > Meta-Learning > Few-Shot Learning

Few-Shot Learning

169 papers with code · Methodology
Subtask of Meta-Learning

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Wandering Within a World: Online Contextualized Few-Shot Learning

9 Jul 2020renmengye/oc-fewshot-public

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting.

FEW-SHOT LEARNING

3
09 Jul 2020

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

8 Jul 2020clovaai/wsolevaluation

In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

FEW-SHOT LEARNING MODEL SELECTION WEAKLY-SUPERVISED OBJECT LOCALIZATION

148
08 Jul 2020

Predicting the Accuracy of a Few-Shot Classifier

8 Jul 2020mbonto/fewshot_generalization

In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples.

FEW-SHOT LEARNING

0
08 Jul 2020

Laplacian Regularized Few-Shot Learning

28 Jun 2020imtiazziko/LaplacianShot

Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.

FEW-SHOT LEARNING GRAPH CLUSTERING

18
28 Jun 2020

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

19 Jun 2020indy-lab/ProtoTransfer

Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.

FEW-SHOT LEARNING SELF-SUPERVISED LEARNING TRANSFER LEARNING

10
19 Jun 2020

Self-supervised Knowledge Distillation for Few-shot Learning

17 Jun 2020brjathu/SKD

Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process.

FEW-SHOT IMAGE CLASSIFICATION METRIC LEARNING

18
17 Jun 2020

Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

6 Jun 2020yhu01/PT-MAP

Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.

FEW-SHOT IMAGE CLASSIFICATION

17
06 Jun 2020

Adaptive Subspaces for Few-Shot Learning

CVPR 2020 chrysts/dsn_fewshot

In this paper, we provide a framework for few-shot learning by introducing dynamic classifiers that are constructed from few samples.

FEW-SHOT IMAGE CLASSIFICATION OBJECT RECOGNITION

16
01 Jun 2020

Attentive Weights Generation for Few Shot Learning via Information Maximization

CVPR 2020 Yiluan/AWGIM

Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness.

FEW-SHOT IMAGE CLASSIFICATION

0
01 Jun 2020