Bag of Tricks for Out-of-Distribution Generalization
It addresses the need for robust and generalizable deep learning models in real-world applications, though it is incremental as it builds on existing strategies with a focus on simplicity and efficiency.
The paper tackles the problem of out-of-distribution generalization by proposing a simple, memory-efficient learning framework that combines multiple tricks, achieving Top-1 accuracies of 88.16% on public and 75.65% on private test sets and ranking 1st in the nicochallenge-2022 domain generalization task.
Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue. However, most existing algorithms for OOD generalization are complicated and specifically designed for certain dataset. To alleviate this problem, nicochallenge-2022 provides NICO++, a large-scale dataset with diverse context information. In this paper, based on systematic analysis of different schemes on NICO++ dataset, we propose a simple but effective learning framework via coupling bag of tricks, including multi-objective framework design, data augmentations, training and inference strategies. Our algorithm is memory-efficient and easily-equipped, without complicated modules and does not require for large pre-trained models. It achieves an excellent performance with Top-1 accuracy of 88.16% on public test set and 75.65% on private test set, and ranks 1st in domain generalization task of nicochallenge-2022.