Should You Go Deeper? Optimizing Convolutional Neural Network
Architectures without Training by Receptive Field Analysis

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Should You Go Deeper? Optimizing Convolutional Neural Network Architectures without Training by Receptive Field Analysis

23 June 2021

Mats L. Richter

Julius Schöning

Anna Wiedenroth

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Papers citing "Should You Go Deeper? Optimizing Convolutional Neural Network Architectures without Training by Receptive Field Analysis"

7 / 7 papers shown

Title
Convolution and Attention-Free Mamba-based Cardiac Image Segmentation Abbas Khan Muhammad Asad Martin Benning C. Roney Gregory Slabaugh Mamba 71 4 0 09 Jun 2024
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion S. Casarin C. Ugwu Sergio Escalera Oswald Lanz 98 0 0 22 Mar 2024
Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models Pablo Pernias Dominic Rampas Mats L. Richter Christopher Pal Marc Aubreville DiffM VLM 111 45 0 01 Jun 2023
AI-in-the-Loop -- The impact of HMI in AI-based Application Julius Schöning C. Westerkamp 43 4 0 21 Mar 2023
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance Mats L. Richter C. Pal 70 3 0 26 Nov 2022
AI for Closed-Loop Control Systems -- New Opportunities for Modeling, Designing, and Tuning Control Systems Julius Schöning A. Riechmann H. Pfisterer AI4CE 24 14 0 18 Jan 2022
Feature Space Saturation during Training Mats L. Richter Justin Shenk Wolf Byttner Anders Arpteg Mikael Huss FAtt 49 6 0 15 Jun 2020