In the realm of Zero-Shot Learning (ZSL), we address biases in Generalized
Zero-Shot Learning (GZSL) models, which favor seen data. To counter this, we
introduce an end-to-end generative GZSL framework called D$^3$GZSL. This
framework respects seen and synthesized unseen data as in-distribution and
out-of-distribution data, respectively, for a more balanced model. D$^3$GZSL
comprises two core modules: in-distribution dual space distillation (ID$^2$SD)
and out-of-distribution batch distillation (O$^2$DBD). ID$^2$SD aligns
teacher-student outcomes in embedding and label spaces, enhancing learning
coherence. O$^2$DBD introduces low-dimensional out-of-distribution
representations per batch sample, capturing shared structures between seen and
unseen categories. Our approach demonstrates its effectiveness across
established GZSL benchmarks, seamlessly integrating into mainstream generative
frameworks. Extensive experiments consistently showcase that D$^3$GZSL elevates
the performance of existing generative GZSL methods, underscoring its potential
to refine zero-shot learning practices.The code is available at:
this https URL

通过引入端到端生成式 GZSL 框架 D^3GZSL 并采用 ID^2SD 和 O^2DBD 两个核心模块，我们解决了 GZSL 模型中偏向于已见数据的偏见问题，并通过将已见和合成的未见数据作为内分布和外分布数据分别对待，以实现模型的更好平衡。D^3GZSL 在嵌入和标签空间中对齐教师和学生的结果，增强了学习的一致性，并且通过在每个批次样本中引入低维度的外分布表示，捕捉了已见和未见类别之间的共享结构。我们的方法在已有的 GZSL 基准测试中展示了其有效性，并可以无缝地集成到主流的生成式框架中。大量实验一致表明，D^3GZSL 提升了现有生成式 GZSL 方法的性能，凸显了其优化零样本学习实践的潜力。