Search Results for author:
Found 7 papers, 2 papers with code
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?
This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways?
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways?
Punjabi to Urdu Machine Translation System
For example, during the development of Punjabi to Urdu MTS, many issues were recognized while preparing lexical resources for both the language.
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
The semantic plane detection branch is based on a single-view plane detection framework but with differences.
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner.
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth
Multi-task learning (MTL) paradigm focuses on jointly learning two or more tasks, aiming for significant improvement w. r. t model's generalizability, performance, and training/inference memory footprint.
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.
