{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:26:11Z","timestamp":1777703171945,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge. Although it is inevitable for continual-learning systems to encounter such unseen concepts, the corresponding literature appears to nonetheless focus primarily on alleviating catastrophic interference with learned representations. In this work, we introduce a probabilistic approach that connects these perspectives based on variational inference in a single deep autoencoder model. Specifically, we propose to bound the approximate posterior by fitting regions of high density on the basis of correctly classified data points. These bounds are shown to serve a dual purpose: unseen unknown out-of-distribution data can be distinguished from already trained known tasks towards robust application. Simultaneously, to retain already acquired knowledge, a generative replay process can be narrowed to strictly in-distribution samples, in order to significantly alleviate catastrophic interference.<\/jats:p>","DOI":"10.3390\/jimaging8040093","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T21:34:29Z","timestamp":1648762469000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1639-8255","authenticated-orcid":false,"given":"Martin","family":"Mundt","sequence":"first","affiliation":[{"name":"Department of Computer Science and Mathematics, Goethe University, 60323 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iuliia","family":"Pliushch","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Mathematics, Goethe University, 60323 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sagnik","family":"Majumder","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwon","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8842-905X","authenticated-orcid":false,"given":"Visvanathan","family":"Ramesh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Mathematics, Goethe University, 60323 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"ref_1","unstructured":"Boult, T.E., Cruz, S., Dhamija, A.R., Gunther, M., Henrydoss, J., and Scheirer, W.J. (February, January 27). Learning and the Unknown: Surveying Steps Toward Open World Recognition. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Honolulu, HI, USA."},{"key":"ref_2","first-page":"1","article-title":"Lifelong Machine Learning","volume":"Volume 10","author":"Brachman","year":"2016","journal-title":"Synthesis Lectures on Artificial Intelligence and Machine Learning"},{"key":"ref_3","unstructured":"Matan, O., Kiang, R., Stenard, C.E., Boser, B.E., Denker, J., Henderson, D., Hubbard, W., Jackel, L., and LeCun, Y. (1990, January 5\u20137). Handwritten Character Recognition Using Neural Network Architectures. Proceedings of the 4th United States Postal Service Advanced Technology Conference, Washington, DC, USA."},{"key":"ref_4","unstructured":"Hendrycks, D., and Dietterich, T. (2019, January 6\u20139). Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_5","unstructured":"Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J.V., Lakshminarayanan, B., and Snoek, J. (2019, January 8\u201314). Can You Trust Your Model\u2019s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. Proceedings of the Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_6","unstructured":"Nalisnick, E., Matsukawa, A., Teh, Y.W., Gorur, D., and Lakshminarayanan, B. (2019, January 6\u20139). Do Deep Generative Models Know What They Don\u2019t Know?. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","article-title":"Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem","volume":"24","author":"McCloskey","year":"1989","journal-title":"Psychol. Learn. Motiv.-Adv. Res. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1037\/0033-295X.97.2.285","article-title":"Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions","volume":"97","author":"Ratcliff","year":"1990","journal-title":"Psychol. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","article-title":"Towards Open Set Recognition","volume":"35","author":"Scheirer","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/TPAMI.2014.2321392","article-title":"Probability Models For Open Set Recognition","volume":"36","author":"Scheirer","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual Lifelong Learning with Neural Networks: A Review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (July, January 26). Towards Open Set Deep Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_13","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A. (2019, January 8\u201314). Adversarial Examples are not Bugs, they are Features. Proceedings of the Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_14","unstructured":"Shah, H., Tamuly, K., Raghunathan, A., Jain, P., and Netrapalli, P. (2020, January 6\u201312). The Pitfalls of Simplicity Bias in Neural Networks. Proceedings of the Neural Informtation Processing Systems (NeurIPS), Online."},{"key":"ref_15","unstructured":"Kingma, D.P., and Welling, M. (2013, January 2\u20134). Auto-Encoding Variational Bayes. Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, AZ, USA."},{"key":"ref_16","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_17","unstructured":"van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K. (2016, January 19\u201324). Pixel Recurrent Neural Networks. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), New York, NY, USA."},{"key":"ref_18","unstructured":"Gulrajani, I., Kumar, K., Faruk, A., Taiga, A.A., Visin, F., Vazquez, D., and Courville, A. (2017, January 24\u201326). PixelVAE: A Latent Variable Model for Natural Images. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France."},{"key":"ref_19","unstructured":"Huang, H., Li, Z., He, R., Sun, Z., and Tan, T. (2018, January 2\u20138). Introvae: Introspective variational autoencoders for photographic image synthesis. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2018, January 2\u20137). It Takes (Only) Two: Adversarial Generator-Encoder Networks. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11449"},{"key":"ref_21","unstructured":"Zenke, F., Poole, B., and Ganguli, S. (2017, January 6\u201311). Continual Learning Through Synaptic Intelligence. Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Z., and Hoiem, D. (2016, January 8\u201316). Learning without forgetting. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"ref_24","unstructured":"Hinton, G.E., Vinyals, O., and Dean, J. (2014, January 8\u201313). Distilling the Knowledge in a Neural Network. Proceedings of the Neural Information Processing Systems (NeurIPS), Deep Learning Workshop, Montreal, QC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/09540099550039318","article-title":"Catastrophic Forgetting, Rehearsal and Pseudorehearsal","volume":"7","author":"Robins","year":"1995","journal-title":"Connect. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., and Lampert, C.H. (2017). iCaRL: Incremental Classifier and Representation Learning. arXiv.","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mensink, T., Verbeek, J., Perronnin, F., Csurka, G., Mensink, T., Verbeek, J., Perronnin, F., and Csurka, G. (2012, January 7\u201313). Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3_35"},{"key":"ref_28","unstructured":"Bachem, O., Lucic, M., and Krause, A. (2015, January 6\u201311). Coresets for Nonparametric Estimation\u2014The Case of DP-Means. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_29","first-page":"505","article-title":"Hippocampal and neocortical contributions to memory: Advances in the complementary learning systems framework","volume":"6","author":"Norman","year":"2003","journal-title":"Trends Cogn. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1007\/s12559-016-9389-5","article-title":"A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems","volume":"8","author":"Gepperth","year":"2016","journal-title":"Cogn. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","unstructured":"Shin, H., Lee, J.K., Kim, J.J., and Kim, J. (2017, January 4\u20139). Continual Learning with Deep Generative Replay. Proceedings of the Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_33","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_34","unstructured":"Farquhar, S., and Gal, Y. (2018, January 2\u20138). A Unifying Bayesian View of Continual Learning. Proceedings of the Neural Information Processing Systems (NeurIPS), Bayesian Deep Learning Workshop, Montreal, QC, Canada."},{"key":"ref_35","unstructured":"Farquhar, S., and Gal, Y. (2018, January 10\u201315). Towards Robust Evaluations of Continual Learning. Proceedings of the International Conference on Machine Learning (ICML), Lifelong Learning: A Reinforcement Learning Approach Workshop, Stockholm, Sweden."},{"key":"ref_36","unstructured":"Nguyen, C.V., Li, Y., Bui, T.D., and Turner, R.E. (May, January 30). Variational Continual Learning. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_37","unstructured":"Achille, A., Eccles, T., Matthey, L., Burgess, C.P., Watters, N., Lerchner, A., and Higgins, I. (2018, January 2\u20138). Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_38","unstructured":"Liang, S., Li, Y., and Srikant, R. (May, January 30). Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_39","unstructured":"Lee, K., Lee, H., Lee, K., and Shin, J. (May, January 30). Training Confidence-Calibrated Classifiers for Detecting Out-of-Distribution Samples. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_40","unstructured":"Dhamija, A.R., G\u00fcnther, M., and Boult, T.E. (2018, January 2\u20138). Reducing Network Agnostophobia. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","article-title":"A Practical Bayesian Framework","volume":"472","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"ref_42","unstructured":"Gal, Y., and Ghahramani, Z. (2015, January 6\u201311). Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_43","unstructured":"Graves, A. (2011, January 12\u201317). Practical variational inference for neural networks. Proceedings of the Neural Information Processing Systems (NeurIPS), Granada, Spain."},{"key":"ref_44","unstructured":"Higgins, I., Matthey, L., Pal, A., Burgess, C.P., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A. (2017, January 24\u201326). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_45","unstructured":"Tomczak, J.M., and Welling, M. (2018, January 9\u201311). VAE with a vampprior. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Canary Islands, Spain."},{"key":"ref_46","unstructured":"Hoffman, M.D., and Johnson, M.J. (2016, January 5\u201310). ELBO surgery: Yet another way to carve up the variational evidence lower bound. Proceedings of the Neural Information Processing Systems (NeurIPS), Advances in Approximate Bayesian Inference Workshop, Barcelona, Spain."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_48","unstructured":"Burgess, C.P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A. (2017, January 4\u20139). Understanding disentangling in beta-VAE. Proceedings of the Neural Information Processing Systems (NeurIPS), Workshop on Learning Disentangled Representations, Long Beach, CA, USA."},{"key":"ref_49","unstructured":"Mathieu, E., Rainforth, T., Siddharth, N., and Teh, Y.W. (2019, January 10\u201315). Disentangling disentanglement in variational autoencoders. Proceedings of the International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_50","unstructured":"Bauer, M., and Mnih, A. (2019, January 16\u201318). Resampled Priors for Variational Autoencoders. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan."},{"key":"ref_51","unstructured":"Takahashi, H., Iwata, T., Yamanaka, Y., Yamada, M., and Yagi, S. (February, January 27). Variational Autoencoder with Implicit Optimal Priors. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Honolulu, HI, USA."},{"key":"ref_52","unstructured":"Nilsback, M.E., and Zisserman, A. (2006, January 17\u201322). A Visual Vocabulary For Flower Classification. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA."},{"key":"ref_53","unstructured":"Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv."},{"key":"ref_54","unstructured":"Becker, S., Ackermann, M., Lapuschkin, S., M\u00fcller, K.R., and Samek, W. (2018). Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals. arXiv."},{"key":"ref_55","unstructured":"Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D. (2018, January 2\u20138). Deep Learning for Classical Japanese Literature. Proceedings of the Neural Information Processing Systems (NeurIPS), Workshop on Machine Learning for Creativity and Design, Conference, Montreal, QC, Canada."},{"key":"ref_56","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A.Y. (2011, January 12\u201317). Reading Digits in Natural Images with Unsupervised Feature Learning. Proceedings of the Neural Information Processing Systems (NeurIPS), Granada, Spain."},{"key":"ref_57","unstructured":"Krizhevsky, A. (2022, January 22). Learning Multiple Layers of Features from Tiny Images; Technical Report, Toronto. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf."},{"key":"ref_58","unstructured":"Hendrycks, D., and Gimpel, K. (2017, January 24\u201326). A baseline for detecting misclassified and out-of-distribution examples in neural networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2015, January 7\u201312). Towards Open World Recognition. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kemker, R., McClure, M., Abitino, A., Hayes, T., and Kanan, C. (2018, January 2\u20137). Measuring Catastrophic Forgetting in Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11651"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016, January 19\u201322). Wide Residual Networks. Proceedings of the In Proceedings of the British Machine Vision Conference (BMVC), York, UK.","DOI":"10.5244\/C.30.87"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chaudhry, A., Dokania, P.K., Ajanthan, T., and Torr, P.H.S. (2018, January 8\u201314). Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"ref_64","unstructured":"Hu, W., Lin, Z., Liu, B., Tao, C., Tao, Z., Zhao, D., Ma, J., and Yan, R. (2019, January 6\u20139). Overcoming catastrophic forgetting for continual learning via model adaptation. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_65","unstructured":"Wu, C., Herranz, L., Liu, X., Wang, Y., van de Weijer, J., and Raducanu, B. (2018, January 2\u20138). Memory Replay GANs: Learning to generate images from new categories without forgetting. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_66","unstructured":"Zhai, M., Chen, L., Tung, F., He, J., Nawhal, M., and Mori, G. (November, January 27). Lifelong GAN: Continual Learning for Conditional Image Generation. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Welling, M. (2009, January 14\u201318). Herding dynamical weights to learn. Proceedings of the International Conference on Machine Learning (ICML), Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553517"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Prabhu, A., Torr, P., and Dokania, P. (2020, January 23\u201328). GDumb: A Simple Approach that Questions Our Progress in Continual Learning. Proceedings of the European Conference on Computer Vision (ECCV), Online.","DOI":"10.1007\/978-3-030-58536-5_31"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Liu, Y., Su, Y., Liu, A.A., Schiele, B., and Sun, Q. (2020, January 14\u201319). Mnemonics Training: Multi-Class Incremental Learning without Forgetting. Proceedings of the Computer Vision Pattern Recognition (CVPR), Online.","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cha, H., Lee, J., and Shin, J. (2021, January 11\u201317). Co2L: Contrastive Continual Learning. Proceedings of the International Conference on Computer Vision (ICCV), online.","DOI":"10.1109\/ICCV48922.2021.00938"},{"key":"ref_71","unstructured":"Buzzega, P., Boschini, M., Porrello, A., Abati, D., and Calderara, S. (2020, January 6\u201312). Dark Experience for General Continual Learning: A Strong, Simple Baseline. Proceedings of the Neural Information Processing Systems (NeurIPS), Online."},{"key":"ref_72","unstructured":"Kingma, D.P., Rezende, D.J., Mohamed, S., and Welling, M. (2014, January 8\u201313). Semi-Supervised Learning with Deep Generative Models. Proceedings of the Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_73","unstructured":"Larsen, A.B.L., Sonderby, S.K., Larochelle, H., and Winther, O. (2016, January 19\u201324). Autoencoding beyond pixels using a learned similarity metric. Proceedings of the International Conference on Machine Learning (ICML), New York, NY, USA."},{"key":"ref_74","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/4\/93\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:47:39Z","timestamp":1760136459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/4\/93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":75,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["jimaging8040093"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8040093","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]}}}