Name
Title | ||
|---|---|---|
Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence |
Abstract | ||
|---|---|---|
Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDM51629.2021.00064 | 2021 IEEE International Conference on Data Mining (ICDM) |
Keywords | DocType | ISSN |
Training,Image recognition,Convolution,Conferences,Image representation,Data mining,Convolutional neural networks | Conference | 1550-4786 |
ISBN | Citations | PageRank |
978-1-6654-2398-4 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahuan Ren | 1 | 19 | 2.22 |
| Zhao Zhang | 2 | 938 | 65.99 |
| Jicong Fan | 3 | 81 | 9.62 |
| Haijun Zhang | 4 | 0 | 1.01 |
| Mingliang Xu | 5 | 372 | 54.07 |
| Meng Wang | 6 | 3094 | 167.38 |