Search Results for author:
Found 8 papers, 3 papers with code
Negative Data Augmentation
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #6 on
Image Generation
on CIFAR-100
Efficient Conditional Pre-training for Transfer Learning
Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
Geography-Aware Self-Supervised Learning
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Ranked #5 on
Semantic Segmentation
on SpaceNet 1
(using extra training data)
Efficient Poverty Mapping using Deep Reinforcement Learning
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a texture transfer module to seamlessly integrate the warped try-on cloth onto the target model image.
Understanding Book Popularity on Goodreads
Goodreads has launched the Readers Choice Awards since 2009 where users are able to nominate/vote books of their choice, released in the given year.
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
Understanding and predicting the human visual attentional mechanism is an active area of research in the fields of neuroscience and computer vision.
