A Study on CNN Transfer Learning for Image Classification

By M. Hussain, Jordan J. Bird, D. Faria. UK Workshop on Computational Intelligence, 2018

538 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Transfer learning: a friendly introduction (2022, background): “Object identification for robotic handling, human or object tracking for autonomous cars, and so on are a few examples of the applications of image classification [35].”
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, background): “The beneﬁts of using transfer learning are a decrease in training time, generalization error, and computational cost of building a DL model [28].”
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, background): “The input of the model is an array of images, and the output is the feature vector embedding in a high-dimensional space [73].”