Learning kernels to maximize the power of MMD tests
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Updated
Jan 11, 2018 - Python
Learning kernels to maximize the power of MMD tests
MMD-GAN: Towards Deeper Understanding of Moment Matching Network
Can We Find Strong Lottery Tickets in Generative Models? - Official Code (Pytorch)
Improving MMD-GAN training with repulsive loss function
MXNet Code For Demystifying Neural Style Transfer (IJCAI 2017)
Kernel Change-point Detection with Auxiliary Deep Generative Models (ICLR 2019 paper)
Fast Inference in Denoising Diffusion Models via MMD Finetuning
Maximum mean discrepancy comparisons for single cell profiling experiments
Chapter 11: Transfer Learning/Domain Adaptation
Maximum Mean Discrepancy (MMD), Kernel Stein Discrepancy (KSD), and Fisher Divergence
Implicit generative models and related stuff based on the MMD, in PyTorch
[NeurIPS'25] Sequence Modeling with Spectral Mean Flows, in PyTorch
Official PyTorch implementation of JASA paper "Word-Level Maximum Mean Discrepancy Regularization for Word Embedding"
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"
multi-kernel maximum mean discrepancy
Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition
First GAN compression via NAS + Adaptive Tensor Decomposition
Pytorch implementation of 'Nonlinear Concept Erasure: A Density Matching Approach' (Saillenfest & Lemberger, 2025), Proceedings of ECAI 2025 - 28th European conference on AI
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