论文标题
深度集体学习：在深神网络中共同学习最佳输入和权重
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks
论文作者
论文摘要
可以很好地观察到，在深度学习和计算机视觉文献中，视觉数据始终以手动设计的编码方案表示（例如，当每个通道的整数从0到255）表示，当它们输入到端到端深度神经网络（DNN）时，它们的整数范围为0到255。我们大胆地质疑手动设计的输入是否适合DNN培训不同的任务，并研究是否可以最佳地学习DNN的输入以及学习DNN的权重。在本文中，我们提出了{\ em Deep Collective Learning}的范式，旨在同时学习DNNS的权重，并同时学习DNNS的输入。我们注意到，集体学习已被隐含，但在自然语言处理中广泛使用，而在计算机视觉中几乎从未对其进行研究。因此，我们建议查找视觉网络（查找vnets）作为计算机视觉中深度集体学习的解决方案。这是通过将每个通道中的每个颜色与查找表中的向量关联的。由于现有文献几乎从未研究过计算机视觉中的学习输入，因此我们通过图像分类任务的各种实验探讨了这个问题的几个方面。在四个基准数据集（即CIFAR-10，CIFAR-100，Tiny Imagenet和Imagenet（ILSVRC2012））上进行的实验结果显示了查找VNET的几个令人惊讶的特征，并证明了查找 - VNET和深入集体学习的优势和希望。
It is well observed that in deep learning and computer vision literature, visual data are always represented in a manually designed coding scheme (eg., RGB images are represented as integers ranging from 0 to 255 for each channel) when they are input to an end-to-end deep neural network (DNN) for any learning task. We boldly question whether the manually designed inputs are good for DNN training for different tasks and study whether the input to a DNN can be optimally learned end-to-end together with learning the weights of the DNN. In this paper, we propose the paradigm of {\em deep collective learning} which aims to learn the weights of DNNs and the inputs to DNNs simultaneously for given tasks. We note that collective learning has been implicitly but widely used in natural language processing while it has almost never been studied in computer vision. Consequently, we propose the lookup vision networks (Lookup-VNets) as a solution to deep collective learning in computer vision. This is achieved by associating each color in each channel with a vector in lookup tables. As learning inputs in computer vision has almost never been studied in the existing literature, we explore several aspects of this question through varieties of experiments on image classification tasks. Experimental results on four benchmark datasets, i.e., CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet (ILSVRC2012) have shown several surprising characteristics of Lookup-VNets and have demonstrated the advantages and promise of Lookup-VNets and deep collective learning.
