8 citations · 20 across the 12 of their papers we have counts for
10 papers · 1 filter
Towards Principled Dataset Distillation: A Spectral Distribution Perspective
Ruixi Wu, Shaobo Wang, Jiahuan Chen +9
Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic counterparts for efficient model training. However, existing DD methods exhibit substantial p…
Unsupervised Learning for Class Distribution Mismatch
Pan Du, Wangbo Zhao, Xinai Lu +8
Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Kai Wang, Zekai Li, Zhi-Qi Cheng +6
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios.…
Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicality
Tianle Zhang, Langtian Ma, Yuchen Yan +9
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite t…
ATOM: Attention Mixer for Efficient Dataset Distillation
Samir Khaki, Ahmad Sajedi, Kai Wang +3
Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real datas…
MLLMs-Augmented Visual-Language Representation Learning
Yanqing Liu, Kai Wang, Wenqi Shao +5
Visual-language pre-training has achieved remarkable success in many multi-modal tasks, largely attributed to the availability of large-scale image-text datasets. In this work, we…
Can pre-trained models assist in dataset distillation?
Yao Lu, Xuguang Chen, Yuchen Zhang +7
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile,…
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
Ziyao Guo, Kai Wang, George Cazenavette +3
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on…
CAFE: Learning to Condense Dataset by Aligning Features
Kai Wang, Bo Zhao, Xiangyu Peng +7
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rel…