Learning domain invariant unseen features for generalized zero-shot classification

Leveraging Balanced Semantic Embedding for Generative Zero-Shot Learning

Generative (generalized) zero-shot learning [(G)ZSL] models aim to synthesize unseen class features by using only seen class feature and attribute pairs as training data. However, the generated fake unseen features tend to be dominated by the seen class features and thus classified as seen classes, which can lead to inferior performances under zero-shot learning (ZSL), and unbalanced results under generalized ZSL (GZSL). To address this challenge, we tailor a novel balanced semantic embedding generative network (BSeGN), which incorporates balanced semantic embedding learning into generative learning scenarios in the pursuit of unbiased GZSL. Specifically, we first design a feature-to-semantic embedding module (FEM) to distinguish real seen and fake unseen features collaboratively with the generator in an online manner. We introduce the bidirectional contrastive and balance losses for the FEM learning, which can guarantee a balanced prediction for the interdomain features. In turn, the updated FEM can boost the learning of the generator. Next, we propose a multilevel feature integration module (mFIM) from the cycle-consistency branch of BSeGN, which can mitigate the domain bias through feature enhancement. To the best of our knowledge, this is the first work to explore embedding and generative learning jointly within the field of ZSL. Extensive evaluations on four benchmarks demonstrate the superiority of BSeGN over its state-of-the-art counterparts.

A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously. Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods. However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy. To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS). The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes. Firstly, the model is pretrained by a distance prediction loss to avoid overfitting. Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer. After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability. The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification. Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.