Search Results for author:
Found 10 papers, 7 papers with code
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
We propose a method for editing NeRF scenes with text-instructions.
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation
3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats.
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
Semi-supervised learning from limited quantities of labeled data has shown promise as an alternative.
3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework
The success of deep learning for medical imaging tasks, such as classification, is heavily reliant on the availability of large-scale datasets.
Convolutional Nets for Diabetic Retinopathy Screening in Bangladeshi Patients
This paper is an experimental evaluation of the algorithm we developed for DR diagnosis and screening specifically for Bangladeshi patients.
Window-Level is a Strong Denoising Surrogate
However, those approaches require access to large training sets, specifically the full dose CT images for reference, which can often be difficult to obtain.
Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling
Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing.
Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction
Early detection of suicidal ideation in depressed individuals can allow for adequate medical attention and support, which in many cases is life-saving.
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes.
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images.
