Search Results for author:
Found 10 papers, 8 papers with code
Supervised Contrastive Learning
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
Ranked #2 on
Class Incremental Learning
on cifar100
Unsupervised Training for 3D Morphable Model Regression
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Ranked #2 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Learning Shape Templates with Structured Implicit Functions
To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements.
Local Deep Implicit Functions for 3D Shape
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
Simplified Transfer Learning for Chest Radiography Models Using Less Data
Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph.
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
Our framework is a minimal and conceptually clean synthesis of (C) contrastive learning, (A) masked autoencoders, and (N) the noise prediction approach used in diffusion models.
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs).
Boundless: Generative Adversarial Networks for Image Extension
Image extension models have broad applications in image editing, computational photography and computer graphics.
Ranked #2 on
Uncropping
on Places2 val
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change.
