CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution
Consumer-grade depth sensors provide low-resolution depth maps; however, a high-resolution RGB camera is usually mounted on the same device and acquires a high-resolution image of the same scene. While deep learning and guided filtering methods gave decent results, recent works have highlighted the superiority of using RGB images for Depth Super Resolution. This paper proposes CoReGAN, a generative data fusion model that employs contrastive learning to regularize the extracted features of 2 independent encoders and 1 decoder for Guided Depth Super Resolution, demonstrating state-of-the-art results.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningDecoderDepth Map Super-ResolutionGenerative Adversarial NetworkSuper-ResolutionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Score-Guided Generative Adversarial Networks
We propose a Generative Adversarial Network (GAN) that introduces an evaluator module using pre-trained networks. The proposed model, called score-guided GAN (ScoreGAN), is trained with an evaluation metric for GANs, i.e…
Generative Adversarial NetworkImproving Adversarial Robustness by Contrastive Guided Diffusion Process
Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with stan…
Adversarial RobustnessSynthetic Data GenerationScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation
The promising performance of Deep Neural Networks (DNNs) in text classification, has attracted researchers to use them for fraud review detection. However, the lack of trusted labeled data has limited the performance of …
Data AugmentationGenerative Adversarial NetworkMulti-Task Learningtext-classification+2GGADN: Guided generative adversarial dehazing network
Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent ye…
Image DehazingA Simple Imitation Learning Method via Contrastive Regularization
Learning to imitate expert behavior from demonstrations is a challenging problem, especially in environments with high-dimensional, continuous observations and unknown dynamics. The simplest methods are behavioral clonin…
Atari GamesImitation LearningReinforcement Learning (RL)Representation Learning