论文标题
具有线性竞争单元的随机深网，用于模型远程元学习
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
论文作者
论文摘要
这项工作通过考虑具有随机局部获奖者（LWTA）激活的深层网络来解决元学习（ML）。这种类型的网络单元导致每个模型层的稀疏表示形式，因为单元被组织成仅一个单位生成非零输出的块。引入单位的主要操作原理依赖于随机原理，因为网络在竞争单元上进行后验采样以选择获胜者。因此，与当前标准的确定性表示范式相比，提出的网络是明确设计的，旨在提取稀疏随机性的输入数据表示。我们的方法在几乎没有图像分类和回归实验上产生了最新的预测准确性，并在主动学习设置上降低了预测误差；这些改进的计算成本大大降低。
This work addresses meta-learning (ML) by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units results in sparse representations from each model layer, as the units are organized into blocks where only one unit generates a non-zero output. The main operating principle of the introduced units rely on stochastic principles, as the network performs posterior sampling over competing units to select the winner. Therefore, the proposed networks are explicitly designed to extract input data representations of sparse stochastic nature, as opposed to the currently standard deterministic representation paradigm. Our approach produces state-of-the-art predictive accuracy on few-shot image classification and regression experiments, as well as reduced predictive error on an active learning setting; these improvements come with an immensely reduced computational cost.
