论文标题
基于GPU的自组织地图，用于标记后的几个无监督学习
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
论文作者
论文摘要
在机器学习中，很少有射击分类是一个挑战，目的是使用非常有限的标签示例训练分类器。这种情况可能在现实生活中经常发生，例如，当数据获取或标签价格昂贵时。在这项工作中，我们考虑了标记后的几个无监督学习的问题，这是一项分类任务，其中以无监督的方式学习表示，以后使用很少的带注释的示例标记。我们认为，当嵌入式设备直接获取数据并且执行标签所需的专家无法经常提示时，这个问题很可能会出现在边缘。为了解决这个问题，我们考虑了一种算法，该算法包括使用自组织图（SOM）将转移学习与聚类的串联串联组成。我们引入了基于张量的实现，以加快多核CPU和GPU中的过程。最后，我们使用标准现成的几种分类基准来证明该方法的有效性。
Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires the data, and the expert needed to perform labeling cannot be prompted often. To address this problem, we consider an algorithm consisting of the concatenation of transfer learning with clustering using Self-Organizing Maps (SOMs). We introduce a TensorFlow-based implementation to speed-up the process in multi-core CPUs and GPUs. Finally, we demonstrate the effectiveness of the method using standard off-the-shelf few-shot classification benchmarks.
