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[HTML][HTML] A survey on GANs for computer vision: Recent research, analysis and taxonomy
In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
Closed-loop matters: Dual regression networks for single image super-resolution
Deep neural networks have exhibited promising performance in image super-resolution
(SR) by learning a nonlinear map** function from low-resolution (LR) images to high …
(SR) by learning a nonlinear map** function from low-resolution (LR) images to high …
Scalable optimal transport methods in machine learning: A contemporary survey
Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …
century and has led to a plethora of methods for answering many theoretical and applied …
Dense regression network for video grounding
We address the problem of video grounding from natural language queries. The key
challenge in this task is that one training video might only contain a few annotated …
challenge in this task is that one training video might only contain a few annotated …
Location-aware graph convolutional networks for video question answering
We addressed the challenging task of video question answering, which requires machines
to answer questions about videos in a natural language form. Previous state-of-the-art …
to answer questions about videos in a natural language form. Previous state-of-the-art …
Collaborative unsupervised domain adaptation for medical image diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …
Generative low-bitwidth data free quantization
Neural network quantization is an effective way to compress deep models and improve their
execution latency and energy efficiency, so that they can be deployed on mobile or …
execution latency and energy efficiency, so that they can be deployed on mobile or …
Inter-class and inter-domain semantic augmentation for domain generalization
The domain generalization approach seeks to develop a universal model that performs well
on unknown target domains with the aid of diverse source domains. Data augmentation has …
on unknown target domains with the aid of diverse source domains. Data augmentation has …
One-dm: One-shot diffusion mimicker for handwritten text generation
Existing handwritten text generation methods often require more than ten handwriting
samples as style references. However, in practical applications, users tend to prefer a …
samples as style references. However, in practical applications, users tend to prefer a …