<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning | Trustworthy AI Systems Lab (TAS Lab)</title><link>https://tas-lab.org/tag/deep-learning/</link><atom:link href="https://tas-lab.org/tag/deep-learning/index.xml" rel="self" type="application/rss+xml"/><description>Deep Learning</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Jan 2023 00:00:00 +0000</lastBuildDate><image><url>https://tas-lab.org/media/icon_hu_9cf092d4d003c2c7.png</url><title>Deep Learning</title><link>https://tas-lab.org/tag/deep-learning/</link></image><item><title>Multi-proxy based deep metric learning</title><link>https://tas-lab.org/publication/2023-multi-proxy-based-deep-metric-learning/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2023-multi-proxy-based-deep-metric-learning/</guid><description/></item><item><title>Segmentation Based Backdoor Attack Detection</title><link>https://tas-lab.org/publication/2020-segmentation-based-backdoor-attack-detection/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2020-segmentation-based-backdoor-attack-detection/</guid><description/></item><item><title>Sensitivity Based Robust Learning With Sampling In Adversarial Environment</title><link>https://tas-lab.org/publication/2020-sensitivity-based-robust-learning-with-sampling-in-adversarial-environment/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2020-sensitivity-based-robust-learning-with-sampling-in-adversarial-environment/</guid><description/></item><item><title>Experimental study on stacked autoencoder on insufficient training samples</title><link>https://tas-lab.org/publication/2017-experimental-study-on-stacked-autoencoder-on-insufficient-training-samples/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2017-experimental-study-on-stacked-autoencoder-on-insufficient-training-samples/</guid><description/></item><item><title>Improving robustness of stacked auto-encoder against evasion attack based on weight evenness</title><link>https://tas-lab.org/publication/2017-improving-robustness-of-stacked-auto-encoder-against-evasion-attack-based-on-wei/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2017-improving-robustness-of-stacked-auto-encoder-against-evasion-attack-based-on-wei/</guid><description/></item><item><title>Sensitivity based robust learning for stacked autoencoder against evasion attack</title><link>https://tas-lab.org/publication/2017-sensitivity-based-robust-learning-for-stacked-autoencoder-against-evasion-attack/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2017-sensitivity-based-robust-learning-for-stacked-autoencoder-against-evasion-attack/</guid><description/></item></channel></rss>