Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet. |
Year | Venue | DocType |
|---|---|---|
2020 | AAAI | Conference |
Volume | ISSN | Citations |
34 | 2159-5399 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiabo Huang | 1 | 5 | 1.74 |
| Qi Dong | 2 | 50 | 4.25 |
| Shaogang Gong | 3 | 7941 | 498.04 |
| Xiatian Zhu | 4 | 557 | 37.82 |