论文标题
深度学习中表示的复杂性
Complexity of Representations in Deep Learning
论文作者
论文摘要
深神经网络使用多个函数绘制由输入向量表示的对象逐步映射到不同表示的对象，并通过足够的训练，最终为每个类别的单个分数，这是最终决策功能的输出。理想情况下，在此输出空间中，不同类别的对象实现了最大的分离。由于需要更好地了解深神经网络的内部工作，我们分析了学习表现的有效性，从数据复杂性的角度分离阶级。使用简单的复杂度度量，流行的基准测试任务以及众所周知的体系结构设计，我们展示了数据复杂性如何通过网络演变，培训期间的变化以及如何受到网络设计和培训样本的可用性的影响。我们讨论观察结果的含义和进一步研究的潜力。
Deep neural networks use multiple layers of functions to map an object represented by an input vector progressively to different representations, and with sufficient training, eventually to a single score for each class that is the output of the final decision function. Ideally, in this output space, the objects of different classes achieve maximum separation. Motivated by the need to better understand the inner working of a deep neural network, we analyze the effectiveness of the learned representations in separating the classes from a data complexity perspective. Using a simple complexity measure, a popular benchmarking task, and a well-known architecture design, we show how the data complexity evolves through the network, how it changes during training, and how it is impacted by the network design and the availability of training samples. We discuss the implications of the observations and the potentials for further studies.
