This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}. We accomplish this by proposing a comprehensive generalize-able open-world learning protocol capable of evaluating various components of open-world learning in an operational setting. We argue that in true open-world learning, the underlying feature representation should be learned in a self-supervised manner. Under this self-supervised feature representation, we introduce the problem of detecting unknowns as samples belonging to Out-of-Label space. We differentiate between Out-of-Label space detection and the conventional Out-of-Distribution detection depending upon whether the unknowns being detected belong to the native-world (same as feature representation) or a new-world, respectively. Our unifying open-world learning framework combines three individual research dimensions, which typically have been explored independently, i.e., Incremental Learning, Out-of-Distribution detection and Open-World Learning. Starting from a self-supervised feature space, an open-world learner has the ability to adapt and specialize its feature space to the classes in each incremental phase and hence perform better without incurring any significant overhead, as demonstrated by our experimental results. The incremental learning component of our pipeline provides the new state-of-the-art on established ImageNet-100 protocol. We also demonstrate the adaptability of our approach by showing how it can work as a plug-in with any of the self-supervised feature representation methods.
翻译:本文指出了现有的开放世界学习方法的缺陷,并试图以“开放世界学习”的形式提供完整的全貌。我们通过提出一个全面的通用的开放世界学习协议来实现这一目标,该协议可以分别评估开放世界学习的各个组成部分;我们主张,在真正的开放世界学习中,应当以自我监督的方式学习基本特征的体现方式。在这种自我监督的特征代表方式下,我们引入了探测未知特征作为属于Label空间的样本的问题。我们从自我监督的地貌空间开始,一个开放世界学习者将发现未知空间与常规的流离网络检测区分开来,这取决于被检测到的未知分子是否分别属于本地世界(类似地貌代表)或一个新世界。我们统一的开放世界学习框架将三个单独的研究层面结合起来,通常都是独立探索的,即递增学习、排除差异检测和开放世界学习。从自我监督的地貌空间开始,一个开放世界学习100的普通的探索者有能力在每一个不断升级的轨道上进行改造,并且通过实验阶段展示我们任何不断演化的特殊空间的不断演化。