论文标题
向前兼容的几杆课程学习
Forward Compatible Few-Shot Class-Incremental Learning
论文作者
论文摘要
新颖的类别在我们动态不断变化的世界中经常出现，例如身份验证系统中的新用户，机器学习模型应识别新类，而不会忘记旧类。当新的课堂实例不足时，这种情况变得更具挑战性，这被称为几个阶级学习学习（FSCIL）。当前方法通过使更新的模型类似于旧模型来回顾性地学习增量学习。相比之下，我们建议学习为未来的更新做准备，并为FSCIL提出远期兼容培训（事实）。远期兼容性要求将来的新课程可以根据当前的阶段数据轻松地纳入当前模型，我们试图通过为将来的新课程保留嵌入空间来实现它。详细说明，我们分配了虚拟原型，以挤压已知类别的嵌入并保留新的班级。此外，我们预测可能的新课程并为更新过程做准备。虚拟原型允许该模型在将来接受可能的更新，该模型充当散布在嵌入空间中的代理，以在推理过程中构建更强的分类器。事实有效地将新课程纳入具有前瞻性的新课程，同时拒绝忘记旧课程。广泛的实验验证了事实的最先进的表现。代码可在以下网址找到：https：//github.com/zhoudw-zdw/cvpr22-fact
Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new class instances are insufficient, which is called few-shot class-incremental learning (FSCIL). Current methods handle incremental learning retrospectively by making the updated model similar to the old one. By contrast, we suggest learning prospectively to prepare for future updates, and propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes. In detail, we assign virtual prototypes to squeeze the embedding of known classes and reserve for new ones. Besides, we forecast possible new classes and prepare for the updating process. The virtual prototypes allow the model to accept possible updates in the future, which act as proxies scattered among embedding space to build a stronger classifier during inference. FACT efficiently incorporates new classes with forward compatibility and meanwhile resists forgetting of old ones. Extensive experiments validate FACT's state-of-the-art performance. Code is available at: https://github.com/zhoudw-zdw/CVPR22-Fact
