<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research on Alakh Aggarwal</title><link>https://alakhaggarwal.com/research/</link><description>Recent content in Research on Alakh Aggarwal</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Alakh Aggarwal · Background photo by [Pawel Czerwinski](https://unsplash.com/@pawel_czerwinski) on [Unsplash](https://unsplash.com/photos/an-abstract-black-background-with-wavy-lines-5mhH4db_79I)</copyright><lastBuildDate>Tue, 06 May 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://alakhaggarwal.com/research/index.xml" rel="self" type="application/rss+xml"/><item><title>TexHOI</title><link>https://alakhaggarwal.com/research/texhoi/</link><pubDate>Tue, 06 May 2025 00:00:00 +0000</pubDate><guid>https://alakhaggarwal.com/research/texhoi/</guid><description>IEEE TVCG (CVM 2025), Best Paper Award. Reconstructs the shape and true surface color of an unknown object from a single video of a hand handling it.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://alakhaggarwal.com/research/texhoi/featured.png"/></item><item><title>Layered-Garment Net</title><link>https://alakhaggarwal.com/research/lgn/</link><pubDate>Sun, 04 Dec 2022 00:00:00 +0000</pubDate><guid>https://alakhaggarwal.com/research/lgn/</guid><description>ACCV 2022, with Samsung Research America. From one photo of a person, rebuilds each layer of clothing as separate 3D surfaces that never pass through each other.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://alakhaggarwal.com/research/lgn/featured.png"/></item><item><title>EPD-Net</title><link>https://alakhaggarwal.com/research/epd-net/</link><pubDate>Wed, 09 Sep 2020 00:00:00 +0000</pubDate><guid>https://alakhaggarwal.com/research/epd-net/</guid><description>IEEE IEMTRONICS 2020. A GAN that hides who is in a face image while keeping expression and other non-identifying information.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://alakhaggarwal.com/research/epd-net/featured.png"/></item><item><title>Broad Neural Network for Change Detection</title><link>https://alakhaggarwal.com/research/broad-learning-change-detection/</link><pubDate>Wed, 17 Jul 2019 00:00:00 +0000</pubDate><guid>https://alakhaggarwal.com/research/broad-learning-change-detection/</guid><description>IEMGraph 2018, Springer. My part: a Broad Learning System that keeps adding its own enhancement layers until accuracy stops improving, applied to finding changed pixels in aerial images.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://alakhaggarwal.com/research/broad-learning-change-detection/featured.png"/></item><item><title>Transfer Learning with Synthetic Images</title><link>https://alakhaggarwal.com/research/synthetic-transfer-learning/</link><pubDate>Thu, 26 Apr 2018 00:00:00 +0000</pubDate><guid>https://alakhaggarwal.com/research/synthetic-transfer-learning/</guid><description>NCVPRIPG 2017, Springer, 100+ citations. Fine-tuning an object detector only on rendered images more than doubled its accuracy on real photos.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://alakhaggarwal.com/research/synthetic-transfer-learning/featured.png"/></item></channel></rss>