19 citations · 40 across the 9 of their papers we have counts for
9 papers
PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework
Ning Kang, Shanzhao Qiu, Shifeng Zhang +2
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s…
Split Hierarchical Variational Compression
Tom Ryder, Chen Zhang, Ning Kang +1
Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has pro…
Parallel Neural Local Lossless Compression
Mingtian Zhang, James Townsend, Ning Kang +1
The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) gen…
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Shifeng Zhang, Ning Kang, Tom Ryder +1
It was estimated that the world produced () of data in 2020, resulting in the enormous costs of both data storage and transmission. Fortunately, rece…
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Hang Xu, Ning Kang, Gengwei Zhang +3
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a…
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression
Shifeng Zhang, Chen Zhang, Ning Kang +1
It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their…