Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning

by   Liu Bo, et al.

Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZSL: how to effectively make use of unseen-class samples for training, still remains. Addressing this problem, we first empirically analyze the roles of unseen-class samples with different degrees of hardness in the training process based on the uneven prediction phenomenon found in many ZSL methods, resulting in three observations. Then, we propose two hardness sampling approaches for selecting a subset of diverse and hard samples from a given unseen-class dataset according to these observations. The first one identifies the samples based on the class-level frequency of the model predictions while the second enhances the former by normalizing the class frequency via an approximate class prior estimated by an explored prior estimation algorithm. Finally, we design a new Self-Training framework with Hardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL method could be seamlessly embedded and it is iteratively trained with unseen-class samples selected by the hardness sampling approach. We introduce two typical ZSL methods into the STHS framework and extensive experiments demonstrate that the derived T-ZSL methods outperform many state-of-the-art methods on three public benchmarks. Besides, we note that the unseen-class dataset is separately used for training in some existing transductive generalized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence, we suggest a more strict T-GZSL data setting and establish a competitive baseline on this setting by introducing the proposed STHS framework to T-GZSL.


page 1

page 2

page 3

page 4


An Iterative Co-Training Transductive Framework for Zero Shot Learning

In zero-shot learning (ZSL) community, it is generally recognized that t...

Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling

Open-set recognition (OSR) aims to simultaneously detect unknown-class s...

HardBoost: Boosting Zero-Shot Learning with Hard Classes

This work is a systematical analysis on the so-called hard class problem...

Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector

Zero-shot learning (ZSL) aims to recognize objects from unseen classes, ...

Evolutionary Generalized Zero-Shot Learning

An open problem on the path to artificial intelligence is generalization...

Transductive Zero-Shot Learning with Adaptive Structural Embedding

Zero-shot learning (ZSL) endows the computer vision system with the infe...

Disentangled Generation with Information Bottleneck for Few-Shot Learning

Few-shot learning (FSL), which aims to classify unseen classes with few ...

Please sign up or login with your details

Forgot password? Click here to reset