[1] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The Caltech-UCSD Birds-200-2011 Dataset,” 2011.
[2] S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi, “Fine-Grained Visual Classification of Aircraft,” Jun. 2013, [Online]. Available: http://arxiv.org/abs/1306.5151
[3] A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li, “Novel dataset for fine-grained image categorization,” CVPR Work. Fine-Grained Vis. Categ., pp. 806–813, 2011.
[4] J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3D object representations for fine-grained categorization,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 554–561. doi: 10.1109/ICCVW.2013.77.
[5] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556v6 [cs.CV], pp. 1–14, Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556
[6] K. Zhang, M. Sun, S. Member, and T. X. Han, “Residual Networks of Residual Networks : Multilevel Residual Networks,” vol. 14, no. 8, pp. 1–12, 2016.
[7] T. Y. Lin, A. Roychowdhury, and S. Maji, “Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 6, pp. 1309–1322, 2018, doi: 10.1109/TPAMI.2017.2723400.
[8] Y. Xie, Q. Gong, X. Luan, J. Yan, and J. Zhang, “A Survey of Fine-Grained Visual Categorization Based on Deep Learning,” J. Syst. Eng. Electron., vol. 35, no. 6, pp. 1337–1356, 2023, doi: 10.23919/jsee.2022.000155.
[9] W. Luo, H. Zhang, J. Li, and X. S. Wei, “Learning semantically enhanced feature for fine-grained image classification,” IEEE Signal Process. Lett., vol. 27, pp. 1545–1549, 2020, doi: 10.1109/LSP.2020.3020227.
[10] F. V. Categorization et al., “Attentional Kernel Encoding Networks for Fine-Grained Visual Categorization,” IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 1, pp. 301–314, 2021, doi: 10.1109/TCSVT.2020.2978115.
[11] P. Zhuang, Y. Wang, and Y. Qiao, “Learning attentive pairwise interaction for fine-grained classification,” AAAI 2020 - 34th AAAI Conf. Artif. Intell., no. Bruner, pp. 13130–13137, 2020, doi: 10.1609/aaai.v34i07.7016.
[12] G. Sun, H. Cholakkal, S. Khan, F. S. Khan, and L. Shao, “Fine-grained recognition: Accounting for subtle differences between similar classes,” AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 12047–12054, 2020, doi: 10.1609/aaai.v34i07.6882.
[13] C. Liu, H. Xie, Z. Zha, L. Ma, L. Yu, and Y. Zhang, “Filtration and Distillation : Enhancing Region Attention for Fine-Grained Visual Categorization,” Proc. AAAI Conf. Artif. Intell. Vol. 34. No. 07. 2020., 2020, [Online]. Available: https://scholar.google.com/scholar?cluster=11663869378030505297&hl=en&as_sdt=0,5
[14] R. Du, J. Xie, Z. Ma, D. Chang, Y.-Z. Song, and J. Guo, “Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 9521–9535, Dec. 2022, doi: 10.1109/TPAMI.2021.3126668.
[15] M. Wang, P. Zhao, X. Lu, F. Min, and X. Wang, “Fine-Grained Visual Categorization: A Spatial-Frequency Feature Fusion Perspective,” IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 6, pp. 2798–2812, 2023, doi: 10.1109/TCSVT.2022.3227737.
[16] Y. Pu, Y. Han, Y. Wang, J. Feng, C. Deng, and G. Huang, “Fine-Grained Recognition with Learnable Semantic Data Augmentation,” IEEE Trans. Image Process., vol. 33, pp. 3130–3144, 2024, doi: 10.1109/TIP.2024.3364500.
[17] Z. Miao, X. Zhao, J. Wang, Y. Li, and H. Li, “Complemental Attention Multi-Feature Fusion Network for Fine-Grained Classification,” IEEE Signal Process. Lett., vol. 28, pp. 1983–1987, 2021, doi: 10.1109/LSP.2021.3114622.
[18] K. L. and L. F.-F. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, “ImageNet: A Large-Scale Hierarchical Image Database,” IEEE Conf. Comput. Vis. pattern recognition. Ieee, 2009, no. IEEE, pp. 248–255, 2009.
[19] A. Lou, S. Guan, and M. Loew, “CFPNet-M: A light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation,” Comput. Biol. Med., vol. 154, 2023, doi: 10.1016/j.compbiomed.2023.106579.
[20] N. Ibtehaz and M. S. Rahman, “MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Networks, vol. 121, pp. 74–87, 2020, doi: 10.1016/j.neunet.2019.08.025.
[21] H. Xie, Z. Chen, F. Hong, and Z. Liu, “CityDreamer: Compositional Generative Model of Unbounded 3D Cities,” 2024 IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 9666–9675, Sep. 2023, doi: 10.1109/CVPR52733.2024.00923.
[22] P.-Y. Chou, Y.-Y. Kao, and C.-H. Lin, “Fine-grained Visual Classification with High-temperature Refinement and Background Suppression,” no. March, pp. 1–9, 2023, [Online]. Available: http://arxiv.org/abs/2303.06442
[23] L. Liu et al., “Computing Systems for Autonomous Driving: State of the Art and Challenges,” IEEE Internet Things J., vol. 8, no. 8, pp. 6469–6486, 2021, doi: 10.1109/JIOT.2020.3043716.
[24] G. Sen Xie, X. Y. Zhang, S. Yan, and C. L. Liu, “Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation,” IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 6, pp. 1263–1274, 2017, doi: 10.1109/TCSVT.2015.2511543.
[25] Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal Brain Damage (Pruning),” Adv. Neural Inf. Process. Syst., pp. 598–605, 1990.
[26] H. Tang et al., “CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation,” IEEE Access, vol. 12, no. January, pp. 35844–35854, 2024, doi: 10.1109/ACCESS.2024.3373619.
[27] H. Cunningham, A. Ewart, L. Riggs, R. Huben, and L. Sharkey, “Sparse Autoencoders Find Highly Interpretable Features in Language Models,” 12th Int. Conf. Learn. Represent. ICLR 2024, pp. 1–20, 2024.
[28] A. Makelov, G. Lange, and N. Nanda, “Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control,” no. 1, 2024, [Online]. Available: http://arxiv.org/abs/2405.08366
[29] Z. Zhao et al., “Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2023-June, pp. 2369–2377, 2023, doi: 10.1109/CVPRW59228.2023.00234.
[30] X. Sun, N. M. Nasrabadi, and T. D. Tran, “Supervised deep sparse coding networks,” Proc. - Int. Conf. Image Process. ICIP, pp. 346–350, 2018, doi: 10.1109/ICIP.2018.8451701.
[31] V. Papyan, J. Sulam, and M. Elad, “Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding,” pp. 1–13, 2016, [Online]. Available: http://arxiv.org/abs/1607.02009
[32] V. Papyan, J. Sulam, and M. Elad, “Working locally thinking globally: Theoretical guarantees for convolutional sparse coding,” IEEE Trans. Signal Process., vol. 65, no. 21, pp. 5687–5701, 2017, doi: 10.1109/TSP.2017.2733447.
[33] J. Mairal, F. Bach, and J. Ponce, “Sparse modeling for image and vision processing,” Found. Trends Comput. Graph. Vis., vol. 8, no. 2–3, pp. 85–283, 2014, doi: 10.1561/0600000058.
[34] H. Bristow, A. Eriksson, and S. Lucey, “Fast convolutional sparse coding,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., no. 2, pp. 391–398, 2013, doi: 10.1109/CVPR.2013.57.
[35] M. Elad, Sparse and Redundant Representations. New York, NY: Springer New York, 2010. doi: 10.1007/978-1-4419-7011-4.
[36] W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” Adv. Neural Inf. Process. Syst., no. Nips, pp. 2082–2090, 2016.
[37] Y. Zhang et al., “Advancing Model Pruning via Bi-level Optimization,” Adv. Neural Inf. Process. Syst., vol. 35, no. NeurIPS, pp. 1–18, 2022.
[38] J. Frankle and M. Carbin, “The lottery ticket hypothesis: Finding sparse, trainable neural networks,” 7th Int. Conf. Learn. Represent. ICLR 2019, pp. 1–42, 2019.
[39] S. Liu and Z. Wang, “Ten Lessons We Have Learned in the New ‘Sparseland’: A Short Handbook for Sparse Neural Network Researchers,” pp. 1–16, 2023, [Online]. Available: http://arxiv.org/abs/2302.02596
[40] Y. He and L. Xiao, “Structured Pruning for Deep Convolutional Neural Networks: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 5, pp. 2900–2919, 2024, doi: 10.1109/TPAMI.2023.3334614.
[41] S. Rajamanoharan et al., “Improving Dictionary Learning with Gated Sparse Autoencoders,” pp. 1–37, Apr. 2024, [Online]. Available: http://arxiv.org/abs/2404.16014
[42] V. Monga, Y. Li, and Y. C. Eldar, “Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing,” IEEE Signal Process. Mag., vol. 38, no. 2, pp. 18–44, 2021, doi: 10.1109/MSP.2020.3016905.
[43] H. Sreter and R. Giryes, “Learned Convolutional Sparse Coding,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2018-April, pp. 2191–2195, 2018, doi: 10.1109/ICASSP.2018.8462313.
[44] K. Gregor and Y. LeCun, “Learning Fast Approximations of Sparse Coding,” Proc. 27th Int. Confer- ence Mach. Learn. Haifa, Isr. 2010., pp. 399–406, May 2010.
[45] F. Gao, X. Deng, M. Xu, J. Xu, and P. L. Dragotti, “Multi-Modal Convolutional Dictionary Learning,” IEEE Trans. Image Process., vol. 31, pp. 1325–1339, 2022, doi: 10.1109/TIP.2022.3141251.
[46] T. Liu, A. Chaman, D. Belius, and I. Dokmanic, “Learning Multiscale Convolutional Dictionaries for Image Reconstruction,” IEEE Trans. Comput. Imaging, vol. 8, pp. 425–437, 2022, doi: 10.1109/TCI.2022.3175309.
[47] C. Garcia-Cardona and B. Wohlberg, “Convolutional Dictionary Learning: A Comparative Review and New Algorithms,” IEEE Trans. Comput. Imaging, vol. 4, no. 3, pp. 366–381, Sep. 2017, doi: 10.1109/TCI.2018.2840334.
[48] B. Wohlberg, “Convolutional sparse representation of color images,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2016, pp. 57–60. doi: 10.1109/SSIAI.2016.7459174.
[49] B. Wohlberg, “Efficient algorithms for convolutional sparse representations,” IEEE Trans. Image Process., vol. 25, no. 1, pp. 301–315, 2016, doi: 10.1109/TIP.2015.2495260.
[50] Robert Tibshirani, “Regression Shrinkage and Selection via the Lasso,” J. R. Stat. Soc. Ser. B, vol. 58, no. 1, pp. 267–288, 1996, [Online]. Available: https://www.jstor.org/stable/2346178
[51] A. Aberdam, J. Sulam, and M. Elad, “Multi-Layer Sparse Coding: The Holistic Way,” SIAM J. Math. Data Sci., vol. 1, no. 1, pp. 46–77, Apr. 2019, doi: 10.1137/18m1183352.
[52] J. Sulam, A. Aberdam, A. Beck, and M. Elad, “On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 320649, pp. 1–13, 2019, doi: 10.1109/TPAMI.2019.2904255.
[53] V. Papyan, Y. Romano, J. Sulam, and M. Elad, “Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks,” IEEE Signal Process. Mag., vol. 35, no. 4, pp. 72–89, 2018, doi: 10.1109/MSP.2018.2820224.
[54] J. Sulam, V. Papyan, Y. Romano, and M. Elad, “Multilayer convolutional sparse modeling: Pursuit and dictionary learning,” IEEE Trans. Signal Process., vol. 66, no. 15, pp. 4090–4104, 2018, doi: 10.1109/TSP.2018.2846226.
[55] V. Papyan, Y. Romano, and M. Elad, “Convolutional neural networks analyzed via convolutional sparse coding,” J. Mach. Learn. Res., vol. 18, no. 1, pp. 1–52, 2017.
[56] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” IEEE Access, vol. 9, pp. 16591–16603, May 2015, doi: 10.1109/ACCESS.2021.3053408.
[57] F. S. Almalou, F. Razzazi;, and A. Amini, “A Multi‐Layer Convolutional Sparse Network for Pattern Classification Based on Sequential Dictionary Learning,” IET Comput. Vis., vol. 20, no. 1, 2026, doi: https://doi.org/10.1049/cvi2.70055.
[58] S. Chen and D. . Donoho, “Basis Pursuit,” Proc. 1994 28th Asilomar Conf. Signals, Syst. Comput., pp. 41–44, 1994.
[59] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Found. Trends® Mach. Learn., vol. 3, no. 1, pp. 1–122, 2010, doi: 10.1561/2200000016.
[60] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.
[61] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
[62] A. Dubey, O. Gupta, R. Raskar, and N. Naik, “Maximum Entropy Fine-Grained Classification,” 32nd Conf. Neural Inf. Process. Syst. (NeurIPS 2018), Montréal, Canada, no. iii, 2018.
[63] R. Wightman and H. Touvron, “ResNet strikes back : An improved training procedure in timm,” pp. 1–22, 2021.
[64] Y. Wang, V. I. Morariu, and L. S. Davis, “Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4148–4157, 2018, doi: 10.1109/CVPR.2018.00436.
[65] X. He, Y. Peng, and J. Zhao, “Fast Fine-Grained Image Classification via Weakly Supervised Discriminative Localization,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 5, pp. 1394–1407, May 2019, doi: 10.1109/TCSVT.2018.2834480.
[66] Q. Xu, S. Li, J. Wang, B. Jiang, and J. Tang, “Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization,” pp. 1–13, Mar. 2024, [Online]. Available: http://arxiv.org/abs/2403.10298
[67] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.
[68] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Front. Psychol., vol. 4, no. APR, pp. 428–429, Dec. 2015, doi: 10.3389/fpsyg.2013.00124.
[69] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: 10.1109/CVPR.2017.243.
