A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
This addresses a key bottleneck in GZSL for realistic scenarios where unseen data is unavailable, though it appears incremental as it builds on existing gating mechanisms.
The paper tackles the challenge of training a gating mechanism for Generalized Zero-Shot Learning (GZSL) without unseen data by proposing a boundary-based Out-of-Distribution classifier that separates unseen from seen samples using only seen samples, achieving advantages over state-of-the-art methods on five benchmark datasets.
Generalized Zero-Shot Learning (GZSL) is a challenging topic that has promising prospects in many realistic scenarios. Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem. However, training the gate is usually challenging due to the lack of data in the unseen domain. To resolve this problem, in this paper, we propose a boundary based Out-of-Distribution (OOD) classifier which classifies the unseen and seen domains by only using seen samples for training. First, we learn a shared latent space on a unit hyper-sphere where the latent distributions of visual features and semantic attributes are aligned class-wisely. Then we find the boundary and the center of the manifold for each class. By leveraging the class centers and boundaries, the unseen samples can be separated from the seen samples. After that, we use two experts to classify the seen and unseen samples separately. We extensively validate our approach on five popular benchmark datasets including AWA1, AWA2, CUB, FLO and SUN. The experimental results demonstrate the advantages of our approach over state-of-the-art methods.