<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benbrahim, Hayet</style></author><author><style face="normal" font="default" size="100%">Behloul, Ali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fine-tuned Xception for Image Classification on Tiny ImageNet</style></title><secondary-title><style face="normal" font="default" size="100%">2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Fine-tuned Xception for Image Classification on Tiny ImageNet</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">El Oued, Algeria</style></pub-location><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">1-66546-714-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image classification has been one of the most widely topic in artificial intelligence, deep models need larger datasets and powerful hardware to improve the highperformance classification. ImageNet Challenge was started in 2010 to classify 100,000 test images into 1000 different classes. Tiny ImageNet challenge is similar to ImageNet challenge, where images are taken from the standard ImageNet and resized to be 64x64. In this paper a fine-tuned Xception to classify images into the 200 classes is presented using the standard Tiny ImageNet dataset, the down-sampling (64x64) of images and the low similarity inter-class makes feature extraction and classification difficult and more challenging. We used a transfer learning algorithm to fine-tune the Xception architecture using the Extreme version of the Inception module to achieve a high validation accuracy of 65.14%.</style></abstract></record></records></xml>