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Diffusion models have made significant advances recently in high-quality image synthesis and related tasks.
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y. and Schapire, R. E · 1997
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Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Borderline-smote: a new over-sampling method in imbalanced data sets learning
Han, H., Wang, W.-Y., and Mao, B.-H · 2005
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhou, Z.-H. and Liu, X.-Y · 2005
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Exploratory undersampling for class-imbalance learning
Liu, X.-Y., Wu, J., and Zhou, Z.-H · 2008
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Svms modeling for highly imbalanced classification
Tang, Y., Zhang, Y.-Q., Chawla, N. V., and Krasser, S · 2008
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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An instance level analysis of data complexity
Smith, M. R., Martinez, T., and Giraud-Carrier, C · 2014
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Smote–ipf: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
Sáez, J. A., Luengo, J., Stefanowski, J., and Herrera, F · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Stabilizing training of generative adversarial networks through regularization
Roth, K., Lucchi, A., Nowozin, S., and Hofmann, T · 2017
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Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and Van Der Maaten, L · 2018
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Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Decoupling representation and classifier for long-tailed recognition
Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., and Kalantidis, Y · 2019
Cited alongside, same era.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
Disentangling label distribution for long-tailed visual recognition
Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., and Chang, B · 2021
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Self-damaging contrastive learning
Jiang, Z., Chen, T., Mortazavi, B. J., and Wang, Z · 2021
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Deep generative models to counter class imbalance: A model-metric mapping with proportion calibration methodology
Mirza, B., Haroon, D., Khan, B., Padhani, A., and Syed, T. Q · 2021
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Class balancing gan with a classifier in the loop
Rangwani, H., Mopuri, K. R., and Babu, R. V · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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Regularizing generative adversarial networks under limited data
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Cited alongside, same era.
Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
Cited alongside, same era.
Mesa: boost ensemble imbalanced learning with meta-sampler
Liu, Z., Wei, P., Jiang, J., Cao, W., Bian, J., and Chang, Y · 2020
Cited alongside, same era.
Long-tail learning via logit adjustment
Menon, A. K., Jayasumana, S., Rawat, A. S., Jain, H., Veit, A., and Kumar, S · 2020
Cited alongside, same era.
Balanced meta-softmax for long-tailed visual recognition
Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al · 2020
Cited alongside, same era.
Improving the fairness of deep generative models without retraining
Tan, S., Shen, Y., and Zhou, B · 2020
Cited alongside, same era.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Tang, K., Huang, J., and Zhang, H · 2020
Cited alongside, same era.
Tseng, H.-Y., Jiang, L., Liu, C., Yang, M.-H., and Yang, W · 2021
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Self-supervised dense consistency regularization for image-to-image translation
Ko, M., Cha, E., Suh, S., Lee, H., Han, J.-J., Shin, J., and Han, B · 2022
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Targeted supervised contrastive learning for long-tailed recognition
Li, T., Cao, P., Yuan, Y., Fan, L., Yang, Y., Feris, R. S., Indyk, P., and Katabi, D · 2022
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Open long-tailed recognition in a dynamic world
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., and Stella, X. Y · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Diffusion probabilistic modeling for video generation
Yang, R., Srivastava, P., and Mandt, S · 2022
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Balanced contrastive learning for long-tailed visual recognition
Zhu, J., Wang, Z., Chen, J., Chen, Y.-P. P., and Jiang, Y.-G · 2022
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Synthetic data from diffusion models improves imagenet classification
Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., and Fleet, D. J · 2023
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Humans are biased. generative ai is even worse, 2023
Nicoletti, L. and Bass, D · 2023
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Class-balancing diffusion models
Qin, Y., Zheng, H., Yao, J., Zhou, M., and Zhang, Y · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M · 2023
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