Fetching the paper…
Reading the bibliography…
Dataset distillation methods reduce large-scale datasets to smaller sets of synthetic data, preserving sufficient information to quickly train a new model from scratch.
No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Facility location: concepts, models, algorithms and case studies
Reza Zanjirani Farahani and Masoud Hekmatfar · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Herding dynamical weights to learn
Max Welling · 2009
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
Earlier work this paper cites.
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Look, imagine and match: Improving textual-visual cross-modal retrieval with generative models
Jiuxiang Gu, Jianfei Cai, Shafiq R Joty, Li Niu, and Gang Wang · 2018
Earlier work this paper cites.
Learning semantic concepts and order for image and sentence matching
Yan Huang, Qi Wu, Chunfeng Song, and Liang Wang · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Earlier work this paper cites.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2019
Earlier work this paper cites.
Unified visual-semantic embeddings: Bridging vision and language with structured meaning representations
Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, and Wei-Ying Ma · 2019
Earlier work this paper cites.
Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
Cited alongside, same era.
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
Cited alongside, same era.
Probabilistic embeddings for cross-modal retrieval
Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio De Rezende, Yannis Kalantidis, and Diane Larlus · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Cited alongside, same era.
Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Contrasting the landscape of contrastive and non-contrastive learning
Ashwini Pokle, Jinjin Tian, Yuchen Li, and Andrej Risteski · 2022
Later among the works it cites.
On implicit bias in overparameterized bilevel optimization
Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, and Roger B Grosse · 2022
Later among the works it cites.
CAFE: Learning to condense dataset by aligning features
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
Later among the works it cites.
High-dimensional data analysis with low-dimensional models: Principles, computation, and applications
John Wright and Yi Ma · 2022
Later among the works it cites.
Regnet: self-regulated network for image classification
Jing Xu, Yu Pan, Xinglin Pan, Steven Hoi, Zhang Yi, and Zenglin Xu · 2022
Later among the works it cites.
Dataset distillation using neural feature regression
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
Cited alongside, same era.
There is more than meets the eye: Self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge
Francisco Rivera Valverde, Juana Valeria Hurtado, and Abhinav Valada · 2021
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
Cited alongside, same era.
Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
Cited alongside, same era.
Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
Cited alongside, same era.
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
Later among the works it cites.
Vision+ x: A survey on multimodal learning in the light of data
Ye Zhu, Yu Wu, Nicu Sebe, and Yan Yan · 2022
Later among the works it cites.
Scaling up dataset distillation to imagenet-1k with constant memory
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2023
Closest in time.
Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent T. F. Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
Closest in time.
Delving into effective gradient matching for dataset condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu, and David Z Pan · 2023
Closest in time.
Dataset distillation via the wasserstein metric
Haoyang Liu, Tiancheng Xing, Luwei Li, Vibhu Dalal, Jingrui He, and Haohan Wang · 2023
Closest in time.
Filtering, distillation, and hard negatives for vision-language pre-training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan · 2023
Closest in time.
Data distillation: A survey
Noveen Sachdeva and Julian McAuley · 2023
Closest in time.
Pix2map: Cross-modal retrieval for inferring street maps from images
Xindi Wu, KwunFung Lau, Francesco Ferroni, Aljoša Ošep, and Deva Ramanan · 2023
Closest in time.
The modality focusing hypothesis: Towards understanding crossmodal knowledge distillation
Zihui Xue, Zhengqi Gao, Sucheng Ren, and Hang Zhao · 2023
Closest in time.
Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
Closest in time.