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Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Earlier work this paper cites.
Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis
Kumar, K., Desrosiers, C., Siddiqi, K., Colliot, O., and Toews, M · 2017
Earlier work this paper cites.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N · 2018
Earlier work this paper cites.
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., ying Deng, C., Mark, R. G., and Horng, S · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Jordon, J., Wilson, A., and van der Schaar, M · 2020
Earlier work this paper cites.
Anonymization through data synthesis using generative adversarial networks (ads-gan)
Yoon, J., Drumright, L. N., and van der Schaar, M · 2020
Earlier work this paper cites.
Synthetic data in machine learning for medicine and healthcare
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F., and Mahmood, F · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Dhariwal, P., Openai, and Nichol, A · 2021
Earlier work this paper cites.
Improved techniques for training single-image gans
Hinz, T., Fisher, M., Wang, O., and Wermter, S · 2021
Earlier work this paper cites.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Earlier work this paper cites.
Membership inference attacks against synthetic health data
Zhang, Z., Yan, C., and Malin, B. A · 2021
Cited alongside, same era.
How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models
Alaa, A., Van Breugel, B., Saveliev, E. S., and van der Schaar, M · 2022
Cited alongside, same era.
Making the most of text semantics to improve biomedical vision-language processing
Boecking, B., Usuyama, N., Bannur, S., Coelho de Castro, D., Schwaighofer, A., Hyland, S., Wetscherek, M. T., Naumann, T., Nori, A., Alvarez-Valle, J., Poon, H., and Oktay, O · 2022
Cited alongside, same era.
Roentgen: Vision-language foundation model for chest x-ray generation
Chambon, P., Bluethgen, C., Delbrouck, J.-B., Van der Sluijs, R., Połacin, M., Chaves, J. M. Z., Abraham, T. M., Purohit, S., Langlotz, C. P., and Chaudhari, A · 2022
Cited alongside, same era.
TorchXRayVision: A library of chest X-ray datasets and models
What is healthy? generative counterfactual diffusion for lesion localization
Sanchez, P., Kascenas, A., Liu, X., O’Neil, A. Q., and Tsaftaris, S. A · 2022
Later among the works it cites.
LAION-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C. W., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S. R., Crowson, K., Schmidt, L., Kaczmarczyk, R., and Jitsev, J · 2022
Later among the works it cites.
Why patient data cannot be easily forgotten?
Su, R., Liu, X., and Tsaftaris, S. A · 2022
Later among the works it cites.
Synthetic data from diffusion models improves imagenet classification, 2023
Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., and Fleet, D. J · 2023
Closest in time.
Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Cohen, J. P., Viviano, J. D., Bertin, P., Morrison, P., Torabian, P., Guarrera, M., Lungren, M. P., Chaudhari, A., Brooks, R., Hashir, M., and Bertrand, H · 2022
Cited alongside, same era.
Differentially private diffusion models
Dockhorn, T., Cao, T., Vahdat, A., and Kreis, K · 2022
Cited alongside, same era.
Can segmentation models be trained with fully synthetically generated data?
Fernandez, V., Pinaya, W. H. L., Borges, P., Tudosiu, P.-D., Graham, M. S., Vercauteren, T., and Cardoso, M. J · 2022
Cited alongside, same era.
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
Jacenkow, G., O’Neil, A. Q., and Tsaftaris, S. A · 2022
Cited alongside, same era.
Diffusion models for medical image analysis: A comprehensive survey
Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., and Merhof, D · 2022
Cited alongside, same era.
Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Rarity score : A new metric to evaluate the uncommonness of synthesized images
Han, J., Choi, H., Choi, Y., Kim, J., Ha, J.-W., and Choi, J · 2023
Closest in time.
Survey: Leakage and privacy at inference time
Jegorova, M., Kaul, C., Mayor, C., O’Neil, A. Q., Weir, A., Murray-Smith, R., and Tsaftaris, S. A · 2023
Closest in time.
The role of imagenet classes in fréchet inception distance
Kynkäänniemi, T., Karras, T., Aittala, M., Aila, T., and Lehtinen, J · 2023
Closest in time.
Is synthetic data from diffusion models ready for knowledge distillation?, 2023
Li, Z., Li, Y., Zhao, P., Song, R., Li, X., and Yang, J · 2023
Closest in time.
Synthetic data generation: State of the art in health care domain
Murtaza, H., Ahmed, M., Khan, N. F., Murtaza, G., Zafar, S., and Bano, A · 2023
Closest in time.
Diffusion art or digital forgery? investigating data replication in diffusion models
Somepalli, G., Singla, V., Goldblum, M., Geiping, Wu, J., and Goldstein, T · 2023
Closest in time.
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L. and Agrawala, M · 2023
Closest in time.
Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data
Packhäuser, K., Gündel, S., Münster, N., Syben, C., Christlein, V., and Maier, A · 2045
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