257 citations · 325 across the 19 of their papers we have counts for
6 papers · 1 filter
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Shuchen Xue, Mingyang Yi, Weijian Luo +4
Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-cons…
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Weijian Luo, Tianyang Hu, Shifeng Zhang +3
Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained m…
Memory Replay with Data Compression for Continual Learning
Liyuan Wang, Xingxing Zhang, Kuo Yang +7
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves…
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci +1
Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themse…
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…
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…