Although deep networks have recently emerged as the model of choice for many
computer vision problems, in order to yield good results they often require
time-consuming architecture search. To combat the complexity of design choices,
prior work has adopted the principle of modularized design which consists in
defining the network in terms of a composition of topologically identical or
similar building blocks (a.k.a. modules). This reduces architecture search to
the problem of determining the number of modules to compose and how to connect
such modules. Again, for reasons of design complexity and training cost,
previous approaches have relied on simple rules of connectivity, e.g.,
connecting each module to only the immediately preceding module or perhaps to
all of the previous ones. Such simple connectivity rules are unlikely to yield
the optimal architecture for the given problem.
In this work we remove these predefined choices and propose an algorithm to
learn the connections between modules in the network. Instead of being chosen a
priori by the human designer, the connectivity is learned simultaneously with
the weights of the network by optimizing the loss function of the end task
using a modified version of gradient descent. We demonstrate our connectivity
learning method on the problem of multi-class image classification using two
popular architectures: ResNet and ResNeXt. Experiments on four different
datasets show that connectivity learning using our approach yields consistently
higher accuracy compared to relying on traditional predefined rules of
connectivity. Furthermore, in certain settings it leads to significant savings
in number of parameters.

本文提出了一种利用模块化设计原则、通过学习连接来自动优化神经网络架构的算法，在多类图像分类任务中，使用 ResNet 和 ResNeXt 两种常见架构进行实验，结果表明与传统模块连接规则相比，学习连接的方法可以显著提高模型准确性并实现参数数量的节省。

MaskConnect: 梯度下降学习连通性

MaskConnect: Connectivity Learning by Gradient Descent

This paper addresses the task of set prediction using deep learning. This is
important because the output of many computer vision tasks, including image
tagging and object detection, are naturally expressed as sets of entities
rather than vectors. As opposed to a vector, the size of a set is not fixed in
advance, and it is invariant to the ordering of entities within it. We define a
likelihood for a set distribution and learn its parameters using a deep neural
network. We also derive a loss for predicting a discrete distribution
corresponding to set cardinality. Set prediction is demonstrated on the problem
of multi-class image classification. Moreover, we show that the proposed
cardinality loss can also trivially be applied to the tasks of object counting
and pedestrian detection. Our approach outperforms existing methods in all
three cases on standard datasets.

本文介绍了如何使用深度学习进行集预测任务，以较高的准确率对多类图像分类、物体计数和行人检测预测问题进行解决。