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Found 13 papers, 7 papers with code
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original.
Latent Discretization for Continuous-time Sequence Compression
We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.
Image Compression with Product Quantized Masked Image Modeling
In this work, we attempt to bring these lines of research closer by revisiting vector quantization for image compression.
An optimal control perspective on diffusion-based generative modeling
In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals.
Compressing Multisets with Large Alphabets using Bits-Back Coding
Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings.
Lossy Compression for Lossless Prediction
Most data is automatically collected and only ever "seen" by algorithms.
Ranked #1 on
Image Compression
on ImageNet
(using extra training data)
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.
Neural Communication Systems with Bandwidth-limited Channel
Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory.
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose.
Improved Bayesian Compression
Compression of Neural Networks (NN) has become a highly studied topic in recent years.
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models.
Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a problem of great significance.
Soft Weight-Sharing for Neural Network Compression
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices.
