Reappraising Domain Generalization in Neural Networks
- URL: http://arxiv.org/abs/2110.07981v1
- Date: Fri, 15 Oct 2021 10:06:40 GMT
- Title: Reappraising Domain Generalization in Neural Networks
- Authors: Sarath Sivaprasad, Akshay Goindani, Vaibhav Garg, Vineet Gandhi
- Abstract summary: Domain generalization (DG) of machine learning algorithms is defined as their ability to learn a domain agnostic hypothesis from multiple training distributions.
We find that a straightforward Empirical Risk Minimization (ERM) baseline consistently outperforms existing DG methods.
We propose a classwise-DG formulation, where for each class, we randomly select one of the domains and keep it aside for testing.
- Score: 8.06370138649329
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Domain generalization (DG) of machine learning algorithms is defined as their
ability to learn a domain agnostic hypothesis from multiple training
distributions, which generalizes onto data from an unseen domain. DG is vital
in scenarios where the target domain with distinct characteristics has sparse
data for training. Aligning with recent work~\cite{gulrajani2020search}, we
find that a straightforward Empirical Risk Minimization (ERM) baseline
consistently outperforms existing DG methods. We present ablation studies
indicating that the choice of backbone, data augmentation, and optimization
algorithms overshadows the many tricks and trades explored in the prior art.
Our work leads to a new state of the art on the four popular DG datasets,
surpassing previous methods by large margins. Furthermore, as a key
contribution, we propose a classwise-DG formulation, where for each class, we
randomly select one of the domains and keep it aside for testing. We argue that
this benchmarking is closer to human learning and relevant in real-world
scenarios. We comprehensively benchmark classwise-DG on the DomainBed and
propose a method combining ERM and reverse gradients to achieve the
state-of-the-art results. To our surprise, despite being exposed to all domains
during training, the classwise DG is more challenging than traditional DG
evaluation and motivates more fundamental rethinking on the problem of DG.
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