Domain shift differences between training data for deep learning models and
the deployment context can result in severe performance issues for models which
fail to generalize. We study the domain shift problem under the context of
remote photoplethysmography (rPPG), a technique for video-based heart rate
inference. We propose metrics based on model similarity which may be used as a
measure of domain shift, and we demonstrate high correlation between these
metrics and empirical performance. One of the proposed metrics with viable
correlations, DS-diff, does not assume access to the ground truth of the target
domain, i.e. it may be applied to in-the-wild data. To that end, we investigate
a model selection problem in which ground truth results for the evaluation
domain is not known, demonstrating a 13.9% performance improvement over the
average case baseline.

通过模型相似性来衡量领域偏移的指标可用作领域偏移的度量，并且我们展示了这些指标与实际性能之间的高相关性。其中一项具有可行相关性的提议指标为 DS-diff，不需要假设对目标领域的基本事实，即可应用于自然环境数据。因此，我们研究了一个模型选择问题，在该问题中，无法获得评估领域的基本事实结果，并且相对于平均情况基准线，性能提高了 13.9%。

使用深度学习远程光电容积脉搏模型相似性测量领域转变

Measuring Domain Shifts using Deep Learning Remote Photoplethysmography  Model Similarity

The federated learning (FL) paradigm emerges to preserve data privacy during
model training by only exposing clients' model parameters rather than original
data. One of the biggest challenges in FL lies in the non-IID (not identical
and independently distributed) data (a.k.a., data heterogeneity) distributed on
clients. To address this challenge, various personalized FL (pFL) methods are
proposed such as similarity-based aggregation and model decoupling. The former
one aggregates models from clients of a similar data distribution. The later
one decouples a neural network (NN) model into a feature extractor and a
classifier. Personalization is captured by classifiers which are obtained by
local training. To advance pFL, we propose a novel pFedSim (pFL based on model
similarity) algorithm in this work by combining these two kinds of methods.
More specifically, we decouple a NN model into a personalized feature
extractor, obtained by aggregating models from similar clients, and a
classifier, which is obtained by local training and used to estimate client
similarity. Compared with the state-of-the-art baselines, the advantages of
pFedSim include: 1) significantly improved model accuracy; 2) low communication
and computation overhead; 3) a low risk of privacy leakage; 4) no requirement
for any external public information. To demonstrate the superiority of pFedSim,
extensive experiments are conducted on real datasets. The results validate the
superb performance of our algorithm which can significantly outperform
baselines under various heterogeneous data settings.

提出一种结合局部模型聚合与神经网络解耦技术的个性化联邦学习算法 (pFedSim)，在保护数据隐私的前提下，显著提高模型精度且计算和通信开销低。

pFedSim: 面向个性化联邦学习的相似性感知模型集成

pFedSim: Similarity-Aware Model Aggregation Towards Personalized  Federated Learning

In this chapter we take a closer look at the distribution of symbolic
regression models generated by genetic programming in the search space. The
motivation for this work is to improve the search for well-fitting symbolic
regression models by using information about the similarity of models that can
be precomputed independently from the target function. For our analysis, we use
a restricted grammar for uni-variate symbolic regression models and generate
all possible models up to a fixed length limit. We identify unique models and
cluster them based on phenotypic as well as genotypic similarity. We find that
phenotypic similarity leads to well-defined clusters while genotypic similarity
does not produce a clear clustering. By mapping solution candidates visited by
GP to the enumerated search space we find that GP initially explores the whole
search space and later converges to the subspace of highest quality expressions
in a run for a simple benchmark problem.

本论文通过使用一定限制语法的编程方法，对符号回归模型空间的分布及模型之间的相似度进行了分析，研究表明凭借表型相似度，可以得出较为明确的模型分类，为符号回归模型的生成提供重要的信息。