Adversarial Deep Multi-Task Learning Using Semantically Orthogonal Spaces and Application to Facial Attributes Prediction - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Adversarial Deep Multi-Task Learning Using Semantically Orthogonal Spaces and Application to Facial Attributes Prediction

Abstract

Deep learning-based multi-task approaches usually rely on factorizing representation layers up to a certain point, where the network splits into several heads, each one addressing a specific task. Depending on the inter-task correlation, such naive model may or may not allow the tasks to benefit from each others. In this paper, we propose a novel Semantic Orthogonality Spaces (SOS) method for multi-task problems, where each task is predicted using the information from a common subspace that factorizes information among all tasks, as well as a task-specific subspace. We enforce orthogonality between these tasks by applying soft orthogonality constraints, as well as adversarially-learned semantic orthogonality objectives that ensures that predicting one task requires the specific information related to that task. We demonstrate the effectiveness of SOS on synthetic data, as well as for large-scale facial attributes prediction. In particular, we use SOS to craft a lightweight architecture that provides high-end accuracies on CelebA database.
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Dates and versions

hal-04087207 , version 1 (03-05-2023)

Identifiers

Cite

Arnaud Dapogny, Gauthier Tallec, Jules Bonnard, Edouard Yvinec, Kevin Bailly. Adversarial Deep Multi-Task Learning Using Semantically Orthogonal Spaces and Application to Facial Attributes Prediction. 17th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2023), Jan 2023, Waikoloa Beach ,HI, United States. ⟨10.1109/FG57933.2023.10042750⟩. ⟨hal-04087207⟩
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