In this story, Rethinking ImageNet Pre-training, by Facebook AI Research (FAIR), is briefly reviewed.
Pre-training have been used over training from scratch for many papers. However, is the pre-trained knowledge really useful when transferred to other computer vision tasks?
In this story, some facts are discovered:
In this story, Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation, by Nanyang Technological University, is reviewed. In this paper:
This is a paper in 2019 IoTaIS. (Sik-Ho Tsang @ Medium)
In this story, Selective Kernel Networks, SKNet, by Nanjing University of Science and Technology, Momenta, Nanjing University, and Tsinghua University, is reviewed.
In the visual cortex, the receptive field (RF) sizes of neurons in the same area (e.g., V1 region) are different, which enables the neurons to collect multi-scale spatial information in the same processing stage.
RF sizes of neurons are not fixed but modulated by stimulus.
In this paper:
In this story, Reducing the Dimensionality of Data with Neural Networks, autoencoder, by University of Toronto, is briefly reviewed. This is a paper by Prof. Hinton. In this paper:
This is a paper in 2006 JSCIENCE with over 14000 citations. (Sik-Ho Tsang @ Medium)
In this story, UHCTD: A Comprehensive Dataset for Camera Tampering Detection, UHCTD, by University of Houston, is reviewed. In this paper:
This is a paper in 2019 AVSS. (Sik-Ho Tsang @ Medium)
In this paper, Visualizing Similarity Data with a Mixture of Maps, UNI-SNE, by University of Toronto, is briefly reviewed since UNI-SNE is mentioned in t-SNE. This is a paper by Prof. Hinton. In this paper:
This is a paper in 2007 ICAIS with over 100 citations. (Sik-Ho Tsang @ Medium)
In this story, Visualizing Data using t-SNE, t-SNE, by Tilburg University, and University of Toronto, is briefly reviewed. It is a very famous paper by Prof. Hinton. In this paper:
This is a paper in 2008 JMLR with over 17000 citations. (Sik-Ho Tsang @ Medium) It was also presented in 2013 Google TechTalk by author.
In this story, Stochastic Neighbor Embedding, SNE, by University of Toronto, is briefly reviewed. Obviously, this paper is by Prof. Hinton. In this paper:
This is a paper in 2002 NIPS with over 1400 citations. (Sik-Ho Tsang @ Medium)
The aim of SNE is to map the high-dimensional data to low-dimensional space for data visualization.
…
In this story, R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features, R²MRF, by China University of Geosciences, National University of Defense Technology, Zhejiang Normal University, Alibaba Group (U.S.) Inc, and University of Sydney, is reviewed. In this paper:
In this story, Blur Detection via Deep Pyramid Network with Recurrent Distinction Enhanced Modules, DPN, by Shenzhen University, Shenzhen Polytechnic, and Southern Illinois University, is reviewed. In this paper:
This is a paper in 2020 JNEUCOM. (Sik-Ho Tsang @ Medium)
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