Is it enough to optimize CNN architectures on ImageNet?

16 March 2021

Papers citing "Is it enough to optimize CNN architectures on ImageNet?"

11 / 11 papers shown

Title
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features Daniel Neururer Volker Dellwo Thilo Stadelmann 13 2 0 01 Nov 2023
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions Peng Yan Ahmed Abdulkadir Paul-Philipp Luley Matthias Rosenthal Gerrit A. Schatte Benjamin Grewe Thilo Stadelmann AI4TS 34 57 0 11 Jul 2023
Does progress on ImageNet transfer to real-world datasets? Alex Fang Simon Kornblith Ludwig Schmidt VLM 21 34 0 11 Jan 2023
Image Classification with Small Datasets: Overview and Benchmark Lorenzo Brigato Björn Barz Luca Iocchi Joachim Denzler VLM 12 17 0 23 Dec 2022
Analysis of Deep Learning Architectures and Efficacy of Detecting Forest Fires Ryan Marinelli 10 0 0 08 Dec 2022
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning Sharath Girish Debadeepta Dey Neel Joshi Vibhav Vineet S. Shah C. C. T. Mendes Abhinav Shrivastava Yale Song SSL 30 2 0 15 Mar 2022
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification Lorenzo Brigato Björn Barz Luca Iocchi Joachim Denzler 23 16 0 30 Aug 2021
FEAR: A Simple Lightweight Method to Rank Architectures Debadeepta Dey Shital C. Shah Sébastien Bubeck OOD 22 4 0 07 Jun 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 4,453 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 948 20,549 0 17 Apr 2017
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 273 10,214 0 16 Nov 2016