论文标题
使用增量极值机器探索开放世界
Exploring the Open World Using Incremental Extreme Value Machines
论文作者
论文摘要
动态环境需要自适应应用。动态环境中的一个特定的机器学习问题是开放世界的认可。它表征了一个不断变化的域，其中仅在一批培训数据中看到了一些类，只能逐步学习此类批次。开放世界的认可是一项艰巨的任务，据我们所知，只有几种方法可以解决。这项工作引入了对广为人知的极值机器（EVM）的修改，以实现开放世界的认可。我们提出的方法通过在更新过程中忽略未受影响的空间，从而通过部分模型拟合功能扩展了EVM。这将训练时间减少了28。此外，我们还使用加权最大k-set盖提供了修改的模型，以严格限制模型的复杂性，并将计算工作从2.1 s减少到0.6 s。在我们的实验中，我们通过两个新的评估方案严格评估开放性。所提出的方法在图像分类和面部识别任务中实现了约12％的卓越精度和计算效率。
Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces a modification of the widely known Extreme Value Machine (EVM) to enable open world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. This reduces the training time by a factor of 28. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.
