no code implementations • NeurIPS 2019 • Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models, and improved likelihood scores.
no code implementations • 27 Sep 2018 • Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
First, we propose a model that extends variational autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space.
no code implementations • ECCV 2018 • Konstantin Shmelkov, Cordelia Schmid, Karteek Alahari
Generative adversarial networks (GANs) are one of the most popular methods for generating images today.
3 code implementations • ICCV 2017 • Konstantin Shmelkov, Cordelia Schmid, Karteek Alahari
Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i. e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data.
2 code implementations • ICCV 2017 • Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid
Real-time scene understanding has become crucial in many applications such as autonomous driving.
Ranked #2 on
Real-Time Object Detection
on PASCAL VOC 2007