论文标题
通过VAE条件的生成流量进行全面的零射击学习
Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
论文作者
论文摘要
广义的零射击学习（GZSL）旨在通过将知识从语义描述转移到视觉表示来识别所见类和看不见的类。最近的生成方法将GZSL作为缺失的数据问题提出，该问题主要采用gan或vaes来生成视觉特征，以供看不见的类别。但是，甘斯通常会遭受不稳定性的困扰，而VAE只能在观察到的数据的对数可能性上优化下限。为了克服上述局限性，我们诉诸于生成流，这是一个生成模型家族，具有准确的可能性估计。更具体地说，我们为GZSL提出了有条件的生成流，即VAE条件的生成流（VAE-CFLOW）。通过使用VAE，首先将语义描述编码为可拖动的潜在分布，以生成流量优化观察到的视觉特征的确切对数可能性。我们通过i）采用VAE重建目标，确保有条件的潜在分布既有意义又具有阶层歧视性，ii）在VAE后验正则化中释放零均值的约束，iiii）在潜在变量上添加分类正则化。我们的方法在五个众所周知的基准数据集上实现了最先进的GZSL，尤其是在大规模环境的显着改善中。代码在https://github.com/guyuchao/vae-cflow-zsl上发布。
Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes by transferring knowledge from semantic descriptions to visual representations. Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes. However, GANs often suffer from instability, and VAEs can only optimize the lower bound on the log-likelihood of observed data. To overcome the above limitations, we resort to generative flows, a family of generative models with the advantage of accurate likelihood estimation. More specifically, we propose a conditional version of generative flows for GZSL, i.e., VAE-Conditioned Generative Flow (VAE-cFlow). By using VAE, the semantic descriptions are firstly encoded into tractable latent distributions, conditioned on that the generative flow optimizes the exact log-likelihood of the observed visual features. We ensure the conditional latent distribution to be both semantic meaningful and inter-class discriminative by i) adopting the VAE reconstruction objective, ii) releasing the zero-mean constraint in VAE posterior regularization, and iii) adding a classification regularization on the latent variables. Our method achieves state-of-the-art GZSL results on five well-known benchmark datasets, especially for the significant improvement in the large-scale setting. Code is released at https://github.com/guyuchao/VAE-cFlow-ZSL.
