3 citations · 3 across the 2 of their papers we have counts for
6 papers
Unpaired Image Translation via Vector Symbolic Architectures
Justin Theiss, Jay Leverett, Daeil Kim +1
Image-to-image translation has played an important role in enabling synthetic data for computer vision. However, if the source and target domains have a large semantic mismatch, ex…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world imag…
Self-Supervised Real-to-Sim Scene Generation
Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche +4
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Syn…
Meta-Sim: Learning to Generate Synthetic Datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu +6
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled dat…
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Aayush Prakash, Shaad Boochoon, Mark Brophy +5
We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure and context of the scene. In contrast to DR, which pl…
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay, Aayush Prakash, David Acuna +7
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the techniqu…