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Facial Emotion Recognition with Noisy Multi-task Annotations
(2021)2021 IEEE Winter Conference on Applications of Computer Vision (WACV)Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multi-task annotations. For this new problem, we suggest a formulation from the point of joint distribution match ...Conference Paper -
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labelling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion ...Conference Paper -
Neural Architecture Search of SPD Manifold Networks
(2021)Proceedings of the Thirtieth International Joint Conference on Artificial IntelligenceConference Paper -
Spectral Tensor Train Parameterization of Deep Learning Layers
(2021)Proceedings of Machine Learning Research ~ Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight ...Conference Paper -
Neural Architecture Search as Sparse Supernet
(2021)Proceedings of the AAAI Conference on Artificial IntelligenceThis paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse ...Conference Paper