Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Training

28 May 2025

Papers citing "Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Training"

8 / 8 papers shown

Title
EfficientFormer: Vision Transformers at MobileNet Speed Yanyu Li Geng Yuan Yang Wen Eric Hu Georgios Evangelidis Sergey Tulyakov Yanzhi Wang Jian Ren ViT 42 360 0 02 Jun 2022
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 118 40,217 0 22 Oct 2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 32 17,950 0 28 May 2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva Alessandro Sordoni Rémi Tachet des Combes Adam Trischler Yoshua Bengio Geoffrey J. Gordon 70 723 0 12 Dec 2018
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob S. Kligys Bo Chen Menglong Zhu Matthew Tang Andrew G. Howard Hartwig Adam Dmitry Kalenichenko MQ 108 3,090 0 15 Dec 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 95 2,854 0 14 Mar 2017
Deep Networks with Stochastic Depth Gao Huang Yu Sun Zhuang Liu Daniel Sedra Kilian Q. Weinberger 89 2,344 0 30 Mar 2016
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 140 6,628 0 08 Jun 2015