Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization
arxiv(2023)
Abstract
During the past decade, deep neural networks have led to fast-paced progress
and significant achievements in computer vision problems, for both academia and
industry. Yet despite their success, state-of-the-art image classification
approaches fail to generalize well in previously unseen visual contexts, as
required by many real-world applications. In this paper, we focus on this
domain generalization (DG) problem and argue that the generalization ability of
deep convolutional neural networks can be improved by taking advantage of
multi-layer and multi-scaled representations of the network. We introduce a
framework that aims at improving domain generalization of image classifiers by
combining both low-level and high-level features at multiple scales, enabling
the network to implicitly disentangle representations in its latent space and
learn domain-invariant attributes of the depicted objects. Additionally, to
further facilitate robust representation learning, we propose a novel objective
function, inspired by contrastive learning, which aims at constraining the
extracted representations to remain invariant under distribution shifts. We
demonstrate the effectiveness of our method by evaluating on the domain
generalization datasets of PACS, VLCS, Office-Home and NICO. Through extensive
experimentation, we show that our model is able to surpass the performance of
previous DG methods and consistently produce competitive and state-of-the-art
results in all datasets
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined