[12] Philip Haeusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, and Daniel Cremers. Associative deep clustering: Training a classification network with no labels. In German Conference on Pattern Recognition (GCPR), 2019.
[13] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[14] Jiabo Huang, Qi Dong, Shaogang Gong, and Xiatian Zhu. Unsupervised deep learning by neighbourhood discovery. In International Conference on Machine Learning (ICML), 2019.
[15] Jiabo Huang, Qi Dong, Shaogang Gong, and Xiatian Zhu. Unsupervised deep learning via affinity diffusion. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020.
[16] Jiabo Huang, Shaogang Gong, and Xiatian Zhu. Deep semantic clustering by partition confidence maximisation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2020.
[17] Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan. Learning representation for clustering via prototype scattering and positive sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
[18] Herve Jegou, Hedi Harzallah, and Cordelia Schmid. A contextual dissimilarity measure for accurate and efficient image search. In 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.
[19] Xu Ji, Joao F Henriques, and Andrea Vedaldi. Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
[20] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Self-taught metric learning without labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[21] Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Piriavihav. Mean shift for self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
[22] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[23] Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng. Contrastive clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021.
[24] Yunfan Li, Mouxing Yang, Dezhong Peng, Taihao Li, Jiantao Huang, and Xi Peng. Twin contrastive learning for online clustering. International Journal of Computer Vision (IJCV), 2022.
[25] Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, and Satya Krishna Gorti. Guided similarity separation for image retrieval. Advances in Neural Information Processing Systems (NeurIPS), 2019.
[26] Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, and Ioannis Patras. Divclust: Controlling diversity in deep clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[27] KL Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Kossar Pourahmadi, Akshayvarun Subramanya, and Hamed Pirsiavash. Constrained mean shift using distant yet related neighbors for representation learning. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
[28] Andrew Ng, Michael Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems (NeurIPS), 2001.