Search Results for author: Oren Rippel

Found 10 papers, 5 papers with code

PIM: Video Coding using Perceptual Importance Maps

no code implementations20 Dec 2022 Evgenya Pergament, Pulkit Tandon, Oren Rippel, Lubomir Bourdev, Alexander G. Anderson, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Kedar Tatwawadi

The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.

Video Compression

An Interactive Annotation Tool for Perceptual Video Compression

1 code implementation8 May 2022 Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi, Oren Rippel, Lubomir Bourdev, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Alexander G. Anderson

We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.

Video Compression

ELF-VC: Efficient Learned Flexible-Rate Video Coding

no code implementations ICCV 2021 Oren Rippel, Alexander G. Anderson, Kedar Tatwawadi, Sanjay Nair, Craig Lytle, Lubomir Bourdev

In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H. 264, H. 265, AV1) and all ML codecs.

Computational Efficiency MS-SSIM +2

Real-Time Adaptive Image Compression

no code implementations ICML 2017 Oren Rippel, Lubomir Bourdev

We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time.

Image Compression

Metric Learning with Adaptive Density Discrimination

2 code implementations18 Nov 2015 Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev

Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks.

Attribute Classification +3

Spectral Representations for Convolutional Neural Networks

no code implementations NeurIPS 2015 Oren Rippel, Jasper Snoek, Ryan P. Adams

In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful representation in which to model and train convolutional neural networks (CNNs).

Dimensionality Reduction

Avoiding pathologies in very deep networks

2 code implementations24 Feb 2014 David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance.

Gaussian Processes

Learning Ordered Representations with Nested Dropout

1 code implementation5 Feb 2014 Oren Rippel, Michael A. Gelbart, Ryan P. Adams

To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network.


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