Stacked Convolutional Denoising Auto-Encoders for Feature Representation

By Bo Du, Wei Xiong, Jia Wu, Lefei Zhang, Liangpei Zhang, Dacheng Tao. 2017

390 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Wangmeng Zuo
3. Luc Van Gool
4. Xuelong Li
5. Jun Yu
6. Ming-Hsuan Yang
7. Ling Shao
8. Xiaokang Yang

Citations in the corpus

• Deep Identity-aware Transfer of Facial Attributes (2016, background): “ccess in versatile high level vision problems [24], [25], [26], [27], [28], but also exhibited their remarkable power in understanding, generating, and recovering images [16], [29], [30], [31], [32], [33]. In this section, we focus on the task of facial attribute transfer, and briefly survey the CNN ”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, neutral): “[94] came up with stacked convolutional denoising”
• Image denoising using deep CNN with batch renormalization (2020, neutral): “Xu, Li, Ma, & Xu, 2018), image segmentation (Long, Shelhamer, & Darrell, 2015), object detection (Chu, Wu, Gu, & Xu, 2017), image denoising (Du et al., 2017), and image super-resolution (Zhang, Zuo, Chen et al.”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “3) Deep Learning Features: As the active performance of deep learning, almost all the state-of-the-art approaches about remote sensing scene classification are based on CNN [15]–[17], [25], [26], [38]–[42].”
• High-quality hyperspectral reconstruction using a spectral prior (2017, methodology): “It has been successfully applied for object retrieval [Leng et al. 2015], image classification [Zhang et al. 2016], denoising [Du et al. 2016], or real-time correction of multipath interference in time-of-flight imaging [Marco et al. 2017].”
• Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, background): “sentations in the areas of computer vision [21], [22], speech recognition [23], and natural language processing (NLP) [24].”
• Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking (2017, background): “Autoencoders (AEs) [35], [36] can efficiently map the input data onto suitable outputs.”