Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance

26 November 2022

Papers citing "Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance"

9 / 9 papers shown

Title
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention Quang-Trung Truong Duc Thanh Nguyen Binh-Son Hua Sai-Kit Yeung VOS 34 1 0 25 Jan 2024
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture Mehraveh Javan Matthew Toews M. Pedersoli 31 1 0 21 Jun 2023
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 209 487 0 01 Oct 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 173 686 0 22 Apr 2021
High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock Soham De Samuel L. Smith Karen Simonyan VLM 223 512 0 11 Feb 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,460 0 23 Jan 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,561 0 17 Apr 2017
Xception: Deep Learning with Depthwise Separable Convolutions François Chollet MDE BDL PINN 203 14,357 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 249 36,362 0 25 Aug 2016