9 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
VSR Veeravasarapu, Abhishek Goel, Deepak Mittal +1
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-bas…
Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models
Prashnna K Gyawali, Rudra Saha, Linwei Wang +2
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However,…
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Ananya Harsh Jha, Saket Anand, Maneesh Singh +1
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the dis…
Cardiac Motion Analysis by Temporal Flow Graphs
V S R Veeravasarapu, Jayanthi Sivaswamy, Vishanji Karani
Cardiac motion analysis from B-mode ultrasound sequence is a key task in assessing the health of the heart. The paper proposes a new methodology for cardiac motion analysis based o…