Search Results for author: Varun Gulshan

Found 7 papers, 2 papers with code

Cross Modal Distillation for Flood Extent Mapping

no code implementations16 Feb 2023 Shubhika Garg, Ben Feinstein, Shahar Timnat, Vishal Batchu, Gideon Dror, Adi Gerzi Rosenthal, Varun Gulshan

Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery.

Knowledge Distillation

Multimodal contrastive learning for remote sensing tasks

no code implementations6 Sep 2022 Umangi Jain, Alex Wilson, Varun Gulshan

Self-supervised methods have shown tremendous success in the field of computer vision, including applications in remote sensing and medical imaging.

Contrastive Learning

A Machine Learning Data Fusion Model for Soil Moisture Retrieval

1 code implementation20 Jun 2022 Vishal Batchu, Grey Nearing, Varun Gulshan

We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil.

BIG-bench Machine Learning regression +2

Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks

no code implementations19 Nov 2021 Chaitanya Patel, Shashank Sharma, Valerie J. Pasquarella, Varun Gulshan

We find that such techniques significantly improve generalization performance when labeled data is limited and there are geographic domain shifts between the training data and the validation/test data.

Segmentation

Inundation Modeling in Data Scarce Regions

no code implementations11 Oct 2019 Zvika Ben-Haim, Vladimir Anisimov, Aaron Yonas, Varun Gulshan, Yusef Shafi, Stephan Hoyer, Sella Nevo

Flood forecasts are crucial for effective individual and governmental protective action.

Who Said What: Modeling Individual Labelers Improves Classification

1 code implementation26 Mar 2017 Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton

We also show that our method performs better than competing algorithms by Welinder and Perona (2010), and by Mnih and Hinton (2012).

Classification General Classification

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