{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:44:09Z","timestamp":1765619049572,"version":"3.48.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376252"],"award-info":[{"award-number":["62376252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Zhejiang Provincial Natural Science Foundation","award":["LZ22F030003"],"award-info":[{"award-number":["LZ22F030003"]}]},{"name":"Zhejiang Province Leading Geese Plan","award":["2025C02025,2025C01056"],"award-info":[{"award-number":["2025C02025,2025C01056"]}]},{"name":"Zhejiang Province Province-Land Synergy Program","award":["2025SDXT004-3"],"award-info":[{"award-number":["2025SDXT004-3"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02809-4","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T03:30:02Z","timestamp":1759203002000},"page":"10443-10456","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cfdc: a fast zero-shot classifier framework based on CLIP and diffusion models"],"prefix":"10.1007","volume":"16","author":[{"given":"Shengkang","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiying","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinzhong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"2809_CR1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 25"},{"key":"2809_CR2","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J et al (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"key":"2809_CR3","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, et\u00a0al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"22","key":"2809_CR4","doi-asserted-by":"publisher","first-page":"4712","DOI":"10.3390\/rs13224712","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen L, Li S, Bai Q et al (2021) Review of image classification algorithms based on convolutional neural networks. Remote Sens 13(22):4712","journal-title":"Remote Sens"},{"key":"2809_CR5","unstructured":"Tan M (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946. Accessed 15 Mar 2024"},{"key":"2809_CR6","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, et\u00a0al (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"2809_CR7","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR8","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et\u00a0al (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672\u20132680"},{"key":"2809_CR9","unstructured":"Song Y, Ermon S (2019) Generative modeling by estimating gradients of the data distribution. Adv Neural Inf Process Syst 32"},{"key":"2809_CR10","doi-asserted-by":"crossref","unstructured":"Esser P, Chiu J, Atighehchian P, et\u00a0al (2023) Structure and content-guided video synthesis with diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 7346\u20137356","DOI":"10.1109\/ICCV51070.2023.00675"},{"key":"2809_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Q, Song J, Huang X, et\u00a0al (2023) Diffcollage: Parallel generation of large content with diffusion models. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 10188\u201310198","DOI":"10.1109\/CVPR52729.2023.00982"},{"key":"2809_CR12","unstructured":"Gong S, Li M, Feng J, et\u00a0al (2022) Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933. Accessed 15 Mar 2024"},{"key":"2809_CR13","first-page":"39957","volume":"36","author":"T Wu","year":"2023","unstructured":"Wu T, Fan Z, Liu X et al (2023) Ar-diffusion: auto-regressive diffusion model for text generation. Adv Neural Inf Process Syst 36:39957\u201339974","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR14","first-page":"4328","volume":"35","author":"X Li","year":"2022","unstructured":"Li X, Thickstun J, Gulrajani I et al (2022) Diffusion-lm improves controllable text generation. Adv Neural Inf Process Syst 35:4328\u20134343","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR15","doi-asserted-by":"crossref","unstructured":"Li AC, Prabhudesai M, Duggal S, et\u00a0al (2023) Your diffusion model is secretly a zero-shot classifier. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 2206\u20132217","DOI":"10.1109\/ICCV51070.2023.00210"},{"key":"2809_CR16","unstructured":"Radford A, Kim JW, Hallacy C, et\u00a0al (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, PMLR, pp 8748\u20138763"},{"key":"2809_CR17","unstructured":"Song J, Meng C, Ermon S (2020) Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502. Accessed 15 Mar 2024"},{"key":"2809_CR18","unstructured":"Amit T, Shaharbany T, Nachmani E, et\u00a0al (2021) Segdiff: Image segmentation with diffusion probabilistic models. arXiv preprint arXiv:2112.00390. Accessed 15 Mar 2024"},{"key":"2809_CR19","unstructured":"Baranchuk D, Rubachev I, Voynov A, et\u00a0al (2021) Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126. Accessed 15 Mar 2024"},{"key":"2809_CR20","doi-asserted-by":"crossref","unstructured":"Brempong EA, Kornblith S, Chen T, et\u00a0al (2022) Denoising pretraining for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4175\u20134186","DOI":"10.1109\/CVPRW56347.2022.00462"},{"issue":"47","key":"2809_CR21","first-page":"1","volume":"23","author":"J Ho","year":"2022","unstructured":"Ho J, Saharia C, Chan W et al (2022) Cascaded diffusion models for high fidelity image generation. J Mach Learn Res 23(47):1\u201333","journal-title":"J Mach Learn Res"},{"key":"2809_CR22","first-page":"8633","volume":"35","author":"J Ho","year":"2022","unstructured":"Ho J, Salimans T, Gritsenko A et al (2022) Video diffusion models. Adv Neural Inf Process Syst 35:8633\u20138646","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR23","doi-asserted-by":"crossref","unstructured":"Cai R, Yang G, Averbuch-Elor H, et\u00a0al (2020) Learning gradient fields for shape generation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, Springer, Cham. pp 364\u2013381","DOI":"10.1007\/978-3-030-58580-8_22"},{"issue":"24","key":"2809_CR24","doi-asserted-by":"publisher","first-page":"8013","DOI":"10.3390\/s24248013","volume":"24","author":"S Umirzakova","year":"2024","unstructured":"Umirzakova S, Maksimova S, Mardieva S et al (2024) Mira-cap: memory-integrated retrieval-augmented captioning for state-of-the-art image and video captioning. Sensors 24(24):8013","journal-title":"Sensors"},{"key":"2809_CR25","unstructured":"Savinov N, Chung J, Binkowski M, et\u00a0al (2021) Step-unrolled denoising autoencoders for text generation. arXiv preprint arXiv:2112.06749. Accessed 3 Apr 2024"},{"key":"2809_CR26","unstructured":"Yu P, Xie S, Ma X, et\u00a0al (2022) Latent diffusion energy-based model for interpretable text modeling. arXiv preprint arXiv:2206.05895. Accessed 3 Apr 2024"},{"key":"2809_CR27","first-page":"12454","volume":"34","author":"E Hoogeboom","year":"2021","unstructured":"Hoogeboom E, Nielsen D, Jaini P et al (2021) Argmax flows and multinomial diffusion: learning categorical distributions. Adv Neural Inf Process Syst 34:12454\u201312465","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR28","unstructured":"Alcaraz JML, Strodthoff N (2022) Diffusion-based time series imputation and forecasting with structured state space models. arXiv preprint arXiv:2208.09399. Accessed 3 Apr 2024"},{"key":"2809_CR29","unstructured":"Chen N, Zhang Y, Zen H, et\u00a0al (2020) Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713. Accessed 3 Apr 2024"},{"key":"2809_CR30","unstructured":"Kong Z, Ping W, Huang J, et\u00a0al (2020) Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761. Accessed 3 Apr 2024"},{"key":"2809_CR31","unstructured":"Ramesh A, Dhariwal P, Nichol A, et\u00a0al (2022) Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1(2):3. Accessed 7 Jun 2024"},{"key":"2809_CR32","unstructured":"Avdeyev P, Shi C, Tan Y, et\u00a0al (2023) Dirichlet diffusion score model for biological sequence generation. In: International Conference on Machine Learning, PMLR, pp 1276\u20131301"},{"key":"2809_CR33","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia C, Chan W, Saxena S et al (2022) Photorealistic text-to-image diffusion models with deep language understanding. Adv Neural Inf Process Syst 35:36479\u201336494","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR34","unstructured":"Blau T, Ganz R, Kawar B, et\u00a0al (2022) Threat model-agnostic adversarial defense using diffusion models. arXiv preprint arXiv:2207.08089. Accessed 7 Jun 2024"},{"key":"2809_CR35","unstructured":"Carlini N, Tramer F, Dvijotham KD, et\u00a0al (2022) (certified!!) adversarial robustness for free! arXiv preprint arXiv:2206.10550. Accessed 7 Jun 2024"},{"key":"2809_CR36","unstructured":"Kawar B, Ganz R, Elad M (2022) Enhancing diffusion-based image synthesis with robust classifier guidance. arXiv preprint arXiv:2208.08664. Accessed 7 Jun 2024"},{"issue":"4","key":"2809_CR37","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1109\/TMI.2022.3220681","volume":"42","author":"H Chung","year":"2022","unstructured":"Chung H, Lee ES, Ye JC (2022) Mr image denoising and super-resolution using regularized reverse diffusion. IEEE Trans Med Imaging 42(4):922\u2013934","journal-title":"IEEE Trans Med Imaging"},{"key":"2809_CR38","doi-asserted-by":"crossref","unstructured":"Chung H, Sim B, Ye JC (2022b) Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12413\u201312422","DOI":"10.1109\/CVPR52688.2022.01209"},{"key":"2809_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479","volume":"80","author":"H Chung","year":"2022","unstructured":"Chung H, Ye JC (2022) Score-based diffusion models for accelerated mri. Med Image Anal 80:102479","journal-title":"Med Image Anal"},{"issue":"10","key":"2809_CR40","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1109\/TPAMI.2017.2762295","volume":"40","author":"Y Long","year":"2017","unstructured":"Long Y, Liu L, Shen F et al (2017) Zero-shot learning using synthesised unseen visual data with diffusion regularisation. IEEE Trans Pattern Anal Mach Intell 40(10):2498\u20132512","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2809_CR41","unstructured":"Chen T, Kornblith S, Norouzi M, et\u00a0al (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, PMLR, pp 1597\u20131607"},{"key":"2809_CR42","doi-asserted-by":"crossref","unstructured":"Yang C, An Z, Cai L, et\u00a0al (2022) Mutual contrastive learning for visual representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3045\u20133053","DOI":"10.1609\/aaai.v36i3.20211"},{"issue":"2","key":"2809_CR43","first-page":"1","volume":"10","author":"W Wang","year":"2019","unstructured":"Wang W, Zheng VW, Yu H et al (2019) A survey of zero-shot learning: settings, methods, and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201337","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"4","key":"2809_CR44","first-page":"4051","volume":"45","author":"F Pourpanah","year":"2022","unstructured":"Pourpanah F, Abdar M, Luo Y et al (2022) A review of generalized zero-shot learning methods. IEEE Trans Pattern Anal Mach Intell 45(4):4051\u20134070","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2809_CR45","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1007\/s11263-019-01193-1","volume":"128","author":"S Changpinyo","year":"2020","unstructured":"Changpinyo S, Chao WL, Gong B et al (2020) Classifier and exemplar synthesis for zero-shot learning. Int J Comput Vision 128:166\u2013201","journal-title":"Int J Comput Vision"},{"key":"2809_CR46","first-page":"572","volume-title":"European conference on computer vision","author":"X Chen","year":"2020","unstructured":"Chen X, Lan X, Sun F et al (2020) A boundary based out-of-distribution classifier for generalized zero-shot learning. European conference on computer vision. Springer, Cham, pp 572\u2013588"},{"key":"2809_CR47","doi-asserted-by":"crossref","unstructured":"Goyal S, Kumar A, Garg S, et\u00a0al (2023) Finetune like you pretrain: Improved finetuning of zero-shot vision models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 19338\u201319347","DOI":"10.1109\/CVPR52729.2023.01853"},{"key":"2809_CR48","doi-asserted-by":"crossref","unstructured":"Wortsman M, Ilharco G, Kim JW, et\u00a0al (2022) Robust fine-tuning of zero-shot models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7959\u20137971","DOI":"10.1109\/CVPR52688.2022.00780"},{"key":"2809_CR49","doi-asserted-by":"crossref","unstructured":"Ge Y, Ren J, Gallagher A, et\u00a0al (2023) Improving zero-shot generalization and robustness of multi-modal models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11093\u201311101","DOI":"10.1109\/CVPR52729.2023.01067"},{"key":"2809_CR50","doi-asserted-by":"crossref","unstructured":"Huang KH, Ahmad WU, Peng N, et\u00a0al (2021) Improving zero-shot cross-lingual transfer learning via robust training. arXiv preprint arXiv:2104.08645. Accessed 24 Jun 2024","DOI":"10.18653\/v1\/2021.emnlp-main.126"},{"key":"2809_CR51","doi-asserted-by":"crossref","unstructured":"Lester B, Al-Rfou R, Constant N (2021) The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691. Accessed 24 Jun 2024","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"2809_CR52","doi-asserted-by":"crossref","unstructured":"Du Y, Wei F, Zhang Z, et\u00a0al (2022) Learning to prompt for open-vocabulary object detection with vision-language model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 14084\u201314093","DOI":"10.1109\/CVPR52688.2022.01369"},{"key":"2809_CR53","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"2809_CR54","doi-asserted-by":"crossref","unstructured":"Bossard L, Guillaumin M, Van\u00a0Gool L (2014) Food-101\u2013mining discriminative components with random forests. In: Computer vision\u2013ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part VI 13, Springer, Cham. pp 446\u2013461","DOI":"10.1007\/978-3-319-10599-4_29"},{"issue":"11","key":"2809_CR55","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"2809_CR56","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A, et\u00a0al (2012) Cats and dogs. In: 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp 3498\u20133505","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"2809_CR57","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et\u00a0al (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2809_CR58","unstructured":"Krizhevsky A, Hinton G, et\u00a0al (2009) Learning multiple layers of features from tiny images.Handbook of Systemic Autoimmune Diseases, 1(4)."},{"key":"2809_CR59","unstructured":"Coates A, Ng A, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, pp 215\u2013223"},{"key":"2809_CR60","doi-asserted-by":"crossref","unstructured":"Cherti M, Beaumont R, Wightman R, et\u00a0al (2023) Reproducible scaling laws for contrastive language-image learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2818\u20132829","DOI":"10.1109\/CVPR52729.2023.00276"},{"key":"2809_CR61","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, et\u00a0al (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10684\u201310695","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2809_CR62","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2809_CR63","unstructured":"Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pp 6000\u20136010"},{"key":"2809_CR64","unstructured":"Alexey D (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929. Accessed 19 Jul 2024"},{"issue":"1","key":"2809_CR65","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han K, Wang Y, Chen H et al (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87\u2013110","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2809_CR66","unstructured":"Alabdulmohsin IM, Zhai X, Kolesnikov A, et\u00a0al (2024) Getting vit in shape: Scaling laws for compute-optimal model design. Adv Neural Inf Process Syst 36:16406\u201316425"},{"key":"2809_CR67","unstructured":"Xu H, Xie S, Tan XE, et\u00a0al (2023) Demystifying clip data. arXiv preprint arXiv:2309.16671. Accessed 19 Jul 2024"},{"key":"2809_CR68","doi-asserted-by":"crossref","unstructured":"Chen Z, Liu G, Zhang BW, et\u00a0al (2022) Altclip: Altering the language encoder in clip for extended language capabilities. arXiv preprint arXiv:2211.06679. Accessed 19 Jul 2024","DOI":"10.18653\/v1\/2023.findings-acl.552"},{"key":"2809_CR69","unstructured":"Jia C, Yang Y, Xia Y, et\u00a0al (2021) Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning, PMLR, pp 4904\u20134916"},{"key":"2809_CR70","unstructured":"K\u00e4rkk\u00e4inen K, Joo J (2019) Fairface: Face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913. Accessed 27 Sep 2024"},{"key":"2809_CR71","unstructured":"Fang A, Jose AM, Jain A, et\u00a0al (2023) Data filtering networks. arxiv:org\/abs\/2309.17425. Accessed 27 Sep 2024"},{"key":"2809_CR72","unstructured":"Yang Y, Gandhi M, Wang Y, et\u00a0al (2024) A textbook remedy for domain shifts: Knowledge priors for medical image analysis. arXiv preprint arXiv:2405.14839. Accessed 27 Sep 2024"},{"key":"2809_CR73","doi-asserted-by":"crossref","unstructured":"Peebles W, Xie S (2023) Scalable diffusion models with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 4195\u20134205","DOI":"10.1109\/ICCV51070.2023.00387"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02809-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02809-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02809-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:41:32Z","timestamp":1765618892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02809-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":73,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2809"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02809-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,9,30]]},"assertion":[{"value":"25 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}