Zero-Shot Learning posed as a Missing Data Problem

Abstract

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.

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Zero-Shot Learning Through Cross-Modal Transfer
• Evaluation of Output Embeddings for Fine-Grained Image Classification
• Transductive Multi-view Zero-Shot Learning
• Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
• Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning
• Multi-Cue Zero-Shot Learning with Strong Supervision

Cited by in corpus (9)

• AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
• Small Sample Learning in Big Data Era
• Zero-Shot Learning via Class-Conditioned Deep Generative Models
• Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
• Bias-Eliminated Semantic Refinement for Any-Shot Learning
• Zero-shot Learning via Shared-Reconstruction-Graph Pursuit
• MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
• A Large-scale Attribute Dataset for Zero-shot Learning
• A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification