论文标题

通过任务相关的分离和可控样品合成的非生成广义零射击学习

Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis

论文作者

Feng, Yaogong, Huang, Xiaowen, Yang, Pengbo, Yu, Jian, Sang, Jitao

论文摘要

合成伪样品当前是解决广义零摄取学习(GZSL)问题的最有效方法。大多数模型都达到了竞争性能,但仍然存在两个问题:(1)特征令人困惑,整体表示将与任务相关和与任务无关的功能混淆,并且现有模型以生成的方式将它们分解,但是它们是不合理的,无法合成可靠的可靠伪样品与有限样品; (2)分布不确定性,当现有模型合成不确定分布的样本时,需要大量数据,这在有限的可见类样品中导致性能差。在本文中,我们提出了一个非生成模型,以在两个模块中相应地解决这些问题:(1)与任务相关的特征分离,将与任务相关的特征从任务无关的特征中排除,这是通过对域对域的适应性对合理综合的对抗性学习的; (2)可控的伪样品合成,以合成具有某些特征的边缘伪钉和中心假样品,以产生更多的多样性和直观的传递。此外,为了描述在培训过程中看到的限制类样本的新场景,我们进一步制定了一个新的ZSL任务,名为“看到的几个类别的类别和零射击的零镜头学习”(FSZU)(FSZU)。对四个基准测试的广泛实验证明了所提出的方法在GZSL和FSZU任务中具有竞争力。

Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem. Most models achieve competitive performance but still suffer from two problems: (1) Feature confounding, the overall representations confound task-correlated and task-independent features, and existing models disentangle them in a generative way, but they are unreasonable to synthesize reliable pseudo samples with limited samples; (2) Distribution uncertainty, that massive data is needed when existing models synthesize samples from the uncertain distribution, which causes poor performance in limited samples of seen classes. In this paper, we propose a non-generative model to address these problems correspondingly in two modules: (1) Task-correlated feature disentanglement, to exclude the task-correlated features from task-independent ones by adversarial learning of domain adaption towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to synthesize edge-pseudo and center-pseudo samples with certain characteristics towards more diversity generated and intuitive transfer. In addation, to describe the new scene that is the limit seen class samples in the training process, we further formulate a new ZSL task named the 'Few-shot Seen class and Zero-shot Unseen class learning' (FSZU). Extensive experiments on four benchmarks verify that the proposed method is competitive in the GZSL and the FSZU tasks.

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