Zero-shot learning is a new paradigm to classify objects from classes that are not available at training time. Zero-shot learning (ZSL) methods have attracted considerable attention in recent years because of their ability to classify unseen/novel class examples. Most of the existing approaches on ZSL works when all the samples from seen classes are available to train the model, which does not suit real life. In this paper, we tackle this hindrance by developing a generative replay-based continual ZSL (GRCZSL). The proposed method endows traditional ZSL to learn from streaming data and acquire new knowledge without forgetting the previous tasks' gained experience. We handle catastrophic forgetting in GRCZSL by replaying the synthetic samples of seen classes, which have appeared in the earlier tasks. These synthetic samples are synthesized using the trained conditional variational autoencoder (VAE) over the immediate past task. Moreover, we only require the current and immediate previous VAE at any time for training and testing. The proposed GRZSL method is developed for a single-head setting of continual learning, simulating a real-world problem setting. In this setting, task identity is given during training but unavailable during testing. GRCZSL performance is evaluated on five benchmark datasets for the generalized setup of ZSL with fixed and dynamic (incremental class) settings of continual learning. The existing class setting presented recently in the literature is not suitable for a class-incremental setting. Therefore, this paper proposes a new setting to address this issue. Experimental results show that the proposed method significantly outperforms the baseline and the state-of-the-art method and makes it more suitable for real-world applications.
翻译:零点学习是一种新模式,用于对培训时没有的班级对象进行分类。 零点学习方法近年来由于能够对看不见/小类示例进行分类而引起相当的注意。 ZSL 的大多数现有方法都是当所见班级的所有样本都可用于培训模型时使用的, 这不符合真实生活。 在本文中, 我们通过开发基于基因的重播连续 ZSL( GRCZSL) 来克服这一障碍。 拟议的方法使传统的ZSL从流数据中学习并获得新知识, 同时又不忘记以前的任务积累的经验。 我们处理GRCZSL 的灾难性遗忘, 方法是重新播放在早期任务中出现的已看到班级的合成样本。 这些合成样本是使用经过训练的有条件的变异自动编码( VAE) 来合成模型的。 此外, 我们只需要在任何时间以当前和眼前的 VAE( GCSL) 来提供培训和测试。 拟议的GRZSL 方法是用来在不断的班级中进行持续学习的首页设置, 模拟的正值, 正在模拟的G- SLSL 测试期间, 正在用这个任务设置的当前测试中, 。 该任务设置中, 将显示一个不定期的运行状态的状态的方法是对现有的状态, 。