Significant progress has been achieved in automating the design of various
components in deep networks. However, the automatic design of loss functions
for generic tasks with various evaluation metrics remains under-investigated.
Previous works on handcrafting loss functions heavily rely on human expertise,
which limits their extendibility. Meanwhile, existing efforts on searching loss
functions mainly focus on specific tasks and particular metrics, with
task-specific heuristics. Whether such works can be extended to generic tasks
is not verified and questionable. In this paper, we propose AutoLoss-Zero, the
first general framework for searching loss functions from scratch for generic
tasks. Specifically, we design an elementary search space composed only of
primitive mathematical operators to accommodate the heterogeneous tasks and
evaluation metrics. A variant of the evolutionary algorithm is employed to
discover loss functions in the elementary search space. A loss-rejection
protocol and a gradient-equivalence-check strategy are developed so as to
improve the search efficiency, which are applicable to generic tasks. Extensive
experiments on various computer vision tasks demonstrate that our searched loss
functions are on par with or superior to existing loss functions, which
generalize well to different datasets and networks. Code shall be released.

本文提出 AutoLoss-Zero，旨在从头开始搜索通用任务的损失函数。该算法使用基本的数学运算符组成一个初步的搜索空间，并使用变异算法在该搜索空间中发现损失函数。利用该算法，我们在计算机视觉任务上获得了比现有损失函数更好的结果。

AutoLoss-Zero：从零开始搜索通用任务的损失函数

AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks

We evaluate whether features extracted from the activation of a deep
convolutional network trained in a fully supervised fashion on a large, fixed
set of object recognition tasks can be re-purposed to novel generic tasks. Our
generic tasks may differ significantly from the originally trained tasks and
there may be insufficient labeled or unlabeled data to conventionally train or
adapt a deep architecture to the new tasks. We investigate and visualize the
semantic clustering of deep convolutional features with respect to a variety of
such tasks, including scene recognition, domain adaptation, and fine-grained
recognition challenges. We compare the efficacy of relying on various network
levels to define a fixed feature, and report novel results that significantly
outperform the state-of-the-art on several important vision challenges. We are
releasing DeCAF, an open-source implementation of these deep convolutional
activation features, along with all associated network parameters to enable
vision researchers to be able to conduct experimentation with deep
representations across a range of visual concept learning paradigms.

本文介绍了一种基于深度卷积网络提取特征的方法，该方法可以将原来应用于特定任务的深度卷积网络迁移到其他任务中，实现了在多个重要视觉挑战中超越现有技术的效果，并发布了开源工具 DeCAF 和所有相关网络参数。