6 citations · 7 across the 4 of their papers we have counts for
7 papers
Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation
Aditi Agarwal, Anjali Jain, Nikita Saxena +7
Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder f…
Farm-Level, In-Season Crop Identification for India
Ishan Deshpande, Amandeep Kaur Reehal, Chandan Nath +20
Accurate, timely, and farm-level crop type information is paramount for national food security, agricultural policy formulation, and economic planning, particularly in agricultural…
Agricultural Landscape Understanding At Country-Scale
Radhika Dua, Aditi Agarwal, Aishwarya Jayagopal +12
Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management. This requires mapping not…
Fast exploration and learning of latent graphs with aliased observations
Miguel Lazaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan +2
We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent t…
Graph schemas as abstractions for transfer learning, inference, and planning
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra +6
Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neur…
Max-Sliced Wasserstein Distance and its use for GANs
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun +6
Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image…
Generative Modeling using the Sliced Wasserstein Distance
Ishan Deshpande, Ziyu Zhang, Alexander Schwing
Generative Adversarial Nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to…