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Soroosh Shahtalebi

Alumni

Publications

A Remedy For Distributional Shifts Through Expected Domain Translation
Jean-christophe Gagnon-audet
Soroosh Shahtalebi
Frank Rudzicz
Irina Rish
Machine learning models often fail to generalize to unseen domains due to the distributional shifts. A family of such shifts, “correlation… (voir plus) shifts,” is caused by spurious correlations in the data. It is studied under the overarching topic of “domain generalization.” In this work, we employ multi-modal translation networks to tackle the correlation shifts that appear when data is sampled out-of-distribution. Learning a generative model from training domains enables us to translate each training sample under the special characteristics of other possible domains. We show that by training a predictor solely on the generated samples, the spurious correlations in training domains average out, and the invariant features corresponding to true correlations emerge. Our proposed technique, Expected Domain Translation (EDT), is benchmarked on the Colored MNIST dataset and drastically improves the state-of-the-art classification accuracy by 38% with train-domain validation model selection.
2022-05-23
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
Soroosh Shahtalebi
Jean-christophe Gagnon-audet
Touraj Laleh
Mojtaba Faramarzi
Kartik Ahuja
Irina Rish
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data d… (voir plus)istribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as"Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.
2021-06-04
ArXiv (prépublication)
arxiv.org
arxiv.org