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Found 6 papers, 5 papers with code
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.
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.
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs).
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
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
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)
