A deep learning platform has been developed to forecast the occurrence of the
low visibility events or hazes. It is trained by using multi-decadal daily
regional maps of various meteorological and hydrological variables as input
features and surface visibility observations as the targets. To better preserve
the characteristic spatial information of different input features for
training, two branched architectures have recently been developed for the case
of Paris hazes. These new architectures have improved the performance of the
network, producing reasonable scores in both validation and a blind forecasting
evaluation using the data of 2021 and 2022 that have not been used in the
training and validation.

开发了一个深度学习平台，用于预测低能见度事件或雾霾的发生。它通过使用多年来各种气象和水文变量的区域地图作为输入特征和地表能见度观测作为目标进行训练。为了更好地保留不同输入特征的特征空间信息，最近为巴黎雾霾开发了两种分支架构。这些新架构改进了网络性能，在验证和使用 2021 年和 2022 年的数据进行盲预测评估时产生了合理的得分，这些数据未用于训练和验证。

一种用不同特征空间尺度的气象图预测巴黎雾霾发生的分枝深度卷积网络

A Branched Deep Convolutional Network for Forecasting the Occurrence of  Hazes in Paris using Meteorological Maps with Different Characteristic  Spatial Scales

In the context of multi-task learning, neural networks with branched
architectures have often been employed to jointly tackle the tasks at hand.
Such ramified networks typically start with a number of shared layers, after
which different tasks branch out into their own sequence of layers.
Understandably, as the number of possible network configurations is
combinatorially large, deciding what layers to share and where to branch out
becomes cumbersome. Prior works have either relied on ad hoc methods to
determine the level of layer sharing, which is suboptimal, or utilized neural
architecture search techniques to establish the network design, which is
considerably expensive. In this paper, we go beyond these limitations and
propose an approach to automatically construct branched multi-task networks, by
leveraging the employed tasks' affinities. Given a specific budget, i.e. number
of learnable parameters, the proposed approach generates architectures, in
which shallow layers are task-agnostic, whereas deeper ones gradually grow more
task-specific. Extensive experimental analysis across numerous, diverse
multi-tasking datasets shows that, for a given budget, our method consistently
yields networks with the highest performance, while for a certain performance
threshold it requires the least amount of learnable parameters.

本文提出了一种自动构建分支式多任务网络的方法，通过利用任务的亲和性，在特定预算（可学习参数的数量）内生成架构，在给定预算的情况下，我们的方法始终产生性能最高的网络，而在某些性能阈值下，只需要最少的可学习参数。