Method

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://github.com/nchucvml/ADMM-SRNet .

Usage

COMMING SOON

Release Note

• v1.0.0, 2023/07/11

Citation

C. -Y. Chiou, K. -T. Lee, C. -R. Huang and P. -C. Chung, "ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification," in IEEE Transactions on Image Processing, vol. 32, pp. 2843-2856, 2023, doi: 10.1109/TIP.2023.3274488.

Acknowledgements

This work was supported in part by the National Science and Technology Council of Taiwan under Grant NSTC 111-2634-F-006-012, Grant NSTC 111-2628-E-006-011-MY3, Grant NSTC 112-2622-8-006-009-TE1, and Grant MOST 111-2327-B-006-007. We thank to National Center for High-performance Computing (NCHC) for providing computational and storage resources.