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Generalized Zero-Shot Learning

10 papers with code · Methodology
Subtask of Zero-Shot Learning

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Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

CVPR 2019 edgarschnfld/CADA-VAE-PyTorch

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

FEW-SHOT LEARNING GENERALIZED ZERO-SHOT LEARNING

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

5 Dec 2018edgarschnfld/CADA-VAE-PyTorch

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

FEW-SHOT LEARNING GENERALIZED ZERO-SHOT LEARNING

Multi-modal Cycle-consistent Generalized Zero-Shot Learning

ECCV 2018 rfelixmg/frwgan-eccv18

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.

GENERALIZED ZERO-SHOT LEARNING

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

13 May 2016pujols/Zero-shot-learning-journal

Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only.

CALIBRATION FEW-SHOT LEARNING GENERALIZED ZERO-SHOT LEARNING OBJECT RECOGNITION

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

ACL 2019 malllabiisc/EWISE

To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.

GENERALIZED ZERO-SHOT LEARNING KNOWLEDGE GRAPH EMBEDDING WORD SENSE DISAMBIGUATION

Generative Dual Adversarial Network for Generalized Zero-shot Learning

CVPR 2019 stevehuanghe/GDAN

Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem.

GENERALIZED ZERO-SHOT LEARNING METRIC LEARNING

Feature Generating Networks for Zero-Shot Learning

CVPR 2018 akku1506/Feature-Generating-Networks-for-ZSL

Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task.

GENERALIZED ZERO-SHOT LEARNING

Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

CVPR 2019 yuvalatzmon/COSMO

COSMO is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune.

GENERALIZED ZERO-SHOT LEARNING

Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

CVPR 2019 yuvalatzmon/COSMO

COSMO is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune.

GENERALIZED ZERO-SHOT LEARNING

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition

25 Mar 2019hellowangqian/domain-adaptation-capls

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain.

GENERALIZED ZERO-SHOT LEARNING UNSUPERVISED DOMAIN ADAPTATION