1 code implementation • 30 Oct 2022 • Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan
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.
1 code implementation • Radiology 2022 • Andrew B. Sellergren, Christina Chen, Zaid Nabulsi, Yuanzhen Li, Aaron Maschinot, Aaron Sarna, Jenny Huang, Charles Lau, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Florencia Garcia-Vicente, David Melnick, Yun Liu, Krish Eswaran, Daniel Tse, Neeral Beladia, Dilip Krishnan, Shravya Shetty
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.
1 code implementation • 14 Aug 2021 • Andrea Burns, Aaron Sarna, Dilip Krishnan, Aaron Maschinot
Disentangled visual representations have largely been studied with generative models such as Variational AutoEncoders (VAEs).
23 code implementations • NeurIPS 2020 • Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
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
no code implementations • ICCV 2019 • Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T. Freeman
Image extension models have broad applications in image editing, computational photography and computer graphics.
Ranked #2 on Uncropping on Places2 val
2 code implementations • CVPR 2018 • Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman
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)