Fetching the paper…
Reading the bibliography…
Recent deep learning models demand larger datasets, driving the need for dataset distillation to create compact, cost-efficient datasets while maintaining performance.
Complete convergence and the law of large numbers
Hsu, P.-L. and Robbins, H · 1947
Earlier work this paper cites.
The ziggurat method for generating random variables
Marsaglia, G. and Tsang, W. W · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Earlier work this paper cites.
A smaller subset of 10 easily classified classes from imagenet, and a little more french
Howard, J · 2019
Earlier work this paper cites.
An introduction to variational autoencoders
Kingma, D. P., Welling, M., et al · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey, D · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Dataset condensation with gradient matching
Zhao, B., Mopuri, K. R., and Bilen, H · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Zhao, B. and Bilen, H · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
Echo: Efficient dataset condensation by higher-order distribution alignment
Zhang, H., Li, S., Wang, P., Zeng, D., and Ge, S · 2023
Later among the works it cites.
Dataset condensation with distribution matching
Zhao, B. and Bilen, H · 2023
Later among the works it cites.
Improved distribution matching for dataset condensation
Zhao, G., Li, G., Qin, Y., and Yu, Y · 2023
Later among the works it cites.
Exploiting inter-sample and inter-feature relations in dataset distillation
Deng, W., Li, W., Ding, T., Wang, L., Zhang, H., Huang, K., Huo, J., and Gao, Y · 2024
Later among the works it cites.
Scaling rectified flow transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cazenavette, G., Wang, T., Torralba, A., Efros, A. A., and Zhu, J.-Y · 2022
Cited alongside, same era.
Dataset condensation with contrastive signals
Lee, S., Chun, S., Jung, S., Yun, S., and Yoon, S · 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.
Beyond neural scaling laws: beating power law scaling via data pruning
Sorscher, B., Geirhos, R., Shekhar, S., Ganguli, S., and Morcos, A · 2022
Cited alongside, same era.
Cafe: Learning to condense dataset by aligning features
Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., and You, Y · 2022
Cited alongside, same era.
Synthetic data from diffusion models improves imagenet classification
Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., and Fleet, D. J · 2023
Cited alongside, same era.
Scaling up dataset distillation to imagenet-1k with constant memory
Cui, J., Wang, R., Si, S., and Hsieh, C.-J · 2023
Cited alongside, same era.
Gu, J., Vahidian, S., Kungurtsev, V., Wang, H., Jiang, W., You, Y., and Chen, Y · 2024
Later among the works it cites.
Selmatch: Effectively scaling up dataset distillation via selection-based initialization and partial updates by trajectory matching
Lee, Y. and Chung, H. W · 2024
Later among the works it cites.
Generalized large-scale data condensation via various backbone and statistical matching
Shao, S., Yin, Z., Zhou, M., Zhang, X., and Shen, Z · 2024
Later among the works it cites.
D^4M: Dataset Distillation via Disentangled Diffusion Model
Su, D., Hou, J., Gao, W., Tian, Y., and Tang, B · 2024
Later among the works it cites.
On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm
Sun, P., Shi, B., Yu, D., and Lin, T · 2024
Later among the works it cites.
Data pruning via moving-one-sample-out
Tan, H., Wu, S., Du, F., Chen, Y., Wang, Z., Wang, F., and Qi, X · 2024
Later among the works it cites.
Teddy: Efficient large-scale dataset distillation via taylor-approximated matching
Yu, R., Liu, S., Ye, J., and Wang, X · 2024
Later among the works it cites.
Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning
Zhang, X., Du, J., Li, Y., Xie, W., and Zhou, J. T · 2024
Later among the works it cites.
Lazydit: Lazy learning for the acceleration of diffusion transformers
Shen, X., Song, Z., Zhou, Y., et al · 2025
Closest in time.