Home
→
Browsing by Subject

Browsing by Subject "transfer learning"

Now showing items 1-6 of 6

CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds

Chang, Yi; Jing, Xin; Ren, Zhao; Schuller, Björn W. (Lausanne : Frontiers Media, 2022)

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being ...
Damage Segmentation on High-Resolution Coating Images Using a Novel Two-Stage Network Pipeline

Hedrich, Kolja; Hinz, Lennart; Reithmeier, Eduard (Basel : MDPI, 2023)

The automation of inspections in aircraft engines is an ever-increasing growing field of research. In particular, the inspection and quantification of coating damages in confined spaces, usually performed manually with ...
Improving deep learning based semantic segmentation with multi view outliner correction

Peters, Torben; Brenner, Claus; Song, M. (Katlenburg-Lindau : Copernicus Publications, 2020)

The goal of this paper is to use transfer learning for semi supervised semantic segmentation in 2D images: given a pretrained deep convolutional network (DCNN), our aim is to adapt it to a new camera-sensor system by ...
Iterative re-weighted instance transfer for domain adaptation

Paul, Andreas; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2016)

Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) ...
Joint classification of ALS and DIM point clouds

Politz, F.; Sester, M. (Göttingen : Copernicus GmbH, 2019)

National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important ...
Using semantically paired images to improve domain adaptation for the semantic segmentation of aerial images

Gritzner, D.; Ostermann, J. (Katlenburg-Lindau : Copernicus Publications, 2020)

Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which ...

Now showing items 1-6 of 6

Submit new document to repository

Browse

All content
- Communities & Collections
- By Issue Date
- Authors
- Titles
- Subjects
- Subjects (GND)
- DDC
- License
- Type

My Account

Usage Statistics

of the Repository

Browsing by Subject "transfer learning"

Browsing by Subject "transfer learning"

Browse

All content

My Account

Usage Statistics