Data Distribution Distilled Generative Model for Generalized Zero-Shot
  Recognition
Yijie Wang Mingjian Hong Luwen Huangfu Sheng Huang
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:
https://github.com/PJBQ/D3GZSL.git