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Found 10 papers, 5 papers with code
PIM: Video Coding using Perceptual Importance Maps
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.
An Interactive Annotation Tool for Perceptual Video Compression
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.
ELF-VC: Efficient Learned Flexible-Rate Video Coding
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.
Learned Video Compression
We present a new algorithm for video coding, learned end-to-end for the low-latency mode.
Real-Time Adaptive Image Compression
We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time.
Metric Learning with Adaptive Density Discrimination
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.
Spectral Representations for Convolutional Neural Networks
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).
Ranked #169 on
Image Classification
on CIFAR-100
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Ranked #156 on
Image Classification
on CIFAR-10
Avoiding pathologies in very deep networks
Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance.
Learning Ordered Representations with Nested Dropout
To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network.
