Methods proposed in the literature for zero-shot learning (ZSL) are typically suitable for offline learning and cannot continually learn from sequential streaming data. The sequential data comes in the form of tasks during training. Recently, a few attempts have been made to handle this issue and develop continual ZSL (CZSL) methods. However, these CZSL methods require clear task-boundary information between the tasks during training, which is not practically possible. This paper proposes a task-free (i.e., task-agnostic) CZSL method, which does not require any task information during continual learning. The proposed task-free CZSL method employs a variational autoencoder (VAE) for performing ZSL. To develop the CZSL method, we combine the concept of experience replay with knowledge distillation and regularization. Here, knowledge distillation is performed using the training sample's dark knowledge, which essentially helps overcome the catastrophic forgetting issue. Further, it is enabled for task-free learning using short-term memory. Finally, a classifier is trained on the synthetic features generated at the latent space of the VAE. Moreover, the experiments are conducted in a challenging and practical ZSL setup, i.e., generalized ZSL (GZSL). These experiments are conducted for two kinds of single-head continual learning settings: (i) mild setting-: task-boundary is known only during training but not during testing; (ii) strict setting-: task-boundary is not known at training, as well as testing. Experimental results on five benchmark datasets exhibit the validity of the approach for CZSL.
翻译:文献中推荐的零光学习方法( ZSL) 通常适合离线学习, 无法不断从连续流数据中学习。 连续数据以培训中的任务形式出现。 最近, 曾尝试过几次来处理这个问题, 并开发连续的 ZSL (CZSL) 方法。 然而, 这些 CZSL 方法需要在培训期间的任务之间提供明确的任务范围信息, 这实际上是不可能的。 本文建议了一种任务( 即任务- 不可知性) CZSL 方法, 它在持续学习期间不需要任何任务信息。 拟议的无任务 CZSL 方法在进行 ZSL 任务时使用变换自动计算器( VAE ) 。 要开发 CZSL 方法, 我们将经验重现的概念与知识蒸馏和规范结合起来。 这里, 知识蒸馏使用培训样品的暗知识, 基本上有助于克服灾难性的遗忘问题。 此外, 能够使用短期的记忆来进行任务性学习。 最后, 将一个分类器用于在 CSLVASL VSL 的隐藏空间中生成的合成功能, 5 。 在实际的实验中进行两次的实验中进行 。 。 这些实验中, 正在进行 正在进行 进行 。 这些实验 进行 进行 。 这些实验中进行 。 这些实验中进行 的 的 的 正在进行 。 这些实验进行 。 这些实验 。 这些实验 。 这些实验 。 这些实验进行 。 这些实验 。 这些实验 。