SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning

Zero-Shot Learning (ZSL) presents the challenge ofiden-tifying categories not seen during training. This task is crucial in domains where it is costly, prohibited, or simply not feasible to collect training data. ZSL depends on a mapping between the visual space and available semantic information. Prior works learn a mapping between spaces that can be exploited during inference. We contend, however, that the disparity between meticulously curated semantic spaces and the inherently noisy nature of real-world data remains a substantial and unresolved challenge. In this paper, we address this by introducing a Semantic Encoder-Enhanced Representations for Zero-Shot Learning (SEER-ZSL). We propose a hybrid strategy to address the generalization gap. First, we aim to distill meaningful semantic information using a probabilistic encoder, enhancing the semantic consis-tency and robustness. Second, we distill the visual space by exploiting the learned data distribution through an ad-versarially trained generator. Finally, we align the distilled information, enabling a mapping of unseen categories onto the true data manifold. We demonstrate empirically that this approach yields a model that outperforms the state-of-the-art benchmarks in terms of both generalization and bench-marks across diverse settings with small, medium, and large datasets. The complete code is available on GitHub. 2