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Found 4 papers, 2 papers with code
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain.
Efficient Video Deblurring Guided by Motion Magnitude
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur.
E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing
In the computer vision modality, the evaluation results suggest that we achieve new state-of-the-art performance on popular UDA benchmarks such as Office-31 and Office-Home, outperforming the second best-performing algorithms by up to 17. 9%.
Out-of-Distribution Detection
Unsupervised Domain Adaptation
Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network
Real-world video deblurring in real time still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost.
Ranked #34 on
Image Deblurring
on GoPro
(using extra training data)
