Zero-Shot Learning posed as a Missing Data Problem

12/02/2016
by   Bo Zhao, et al.
0

This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way -- our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. In experiments, our method outperforms the state-of-the-art on two popular datasets.

READ FULL TEXT

page 1

page 2

research
11/20/2017

Zero-shot Learning via Shared-Reconstruction-Graph Pursuit

Zero-shot learning (ZSL) aims to recognize objects from novel unseen cla...
research
01/25/2018

Class label autoencoder for zero-shot learning

Existing zero-shot learning (ZSL) methods usually learn a projection fun...
research
07/03/2015

Ridge Regression, Hubness, and Zero-Shot Learning

This paper discusses the effect of hubness in zero-shot learning, when r...
research
12/07/2021

Few-Shot Image Classification Along Sparse Graphs

Few-shot learning remains a challenging problem, with unsatisfactory 1-s...
research
10/29/2018

Imagination Based Sample Construction for Zero-Shot Learning

Zero-shot learning (ZSL) which aims to recognize unseen classes with no ...
research
05/03/2018

Multi-Context Label Embedding

Label embedding plays an important role in zero-shot learning. Side info...
research
03/19/2015

Learning Hypergraph-regularized Attribute Predictors

We present a novel attribute learning framework named Hypergraph-based A...

Please sign up or login with your details

Forgot password? Click here to reset