Learning to See in the Dark

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To…Read More

Accelerating Eulerian Fluid Simulation With Convolutional Networks

When asking those in the VFX industry which processes are the slowest and most compute-intensive fluid simulation is bound to be somewhere towards the top of the list. Physical phenomena such as fire and water are notoriously difficult to control, even in the hands of the most experienced artists, and usually require a significant number…Read More

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept…Read More

Machine Learning Labs

Machine learning in practice can be an arduous task. Managing multiple iterations of code, processing input data, feature engineering, training models, visualizing and tabulating results, performing analysis, and using experience to draw conclusions and adapt the system in a way which might yield improved results. In many cases you feel like you have more promising…Read More