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Found 5 papers, 1 papers with code
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.
Identity-aware Facial Expression Recognition in Compressed Video
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.
Energy-constrained Self-training for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images.
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
