8 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation
Shuyang Wu, Yifu Qiu, Ines P. Nearchou +7
Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at…
Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
Thomas Walker, Octave Mariotti, Amir Vaxman +1
Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations…
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…