Meta-Learning-For-Domain Generalization

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Presented by

Parsa Ashrafi Fashi

Introduction

Domain Shift problem addresses the problem where a model trained on a data distribution cannot perform well when tested on another domain with different distribution. Domain Generalization tries to tackle this problem by producing models that can perform well on unseen target domains. Several approaches have been adapted for the problem, such as training a model for each source domain, extract a domain agnostic component domains and semantic feature learning. Meta-Learning and specifically Model-Agnostic Meta-Learning which have been widely adapted recently, are models capable of adapting or generalizing to new tasks and new environments that have never been encountered during training time. Here by defining tasks as domains, the paper tries to overcome the problem in a model-agnostic way.


Previous Work

Method

Experiments

Results

Conclusion

Critiques

References

[1]: Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NeurIPS (2017)

[2]: Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)

[3]: Kokkinos, I.: Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: CVPR (2017)

[4]: Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: ECCV (2016)