Environment Diversification with Multi-head Neural Network for Invariant Learning

Autor:	Huang, Bo-Wei, Liao, Keng-Te, Kao, Chang-Sheng, Lin, Shou-De
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts. Comment: In Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.08778 Zobrazit plný text záznamu View this record from Arxiv