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Found 8 papers, 2 papers with code
Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness
Based on our findings, we propose Purify++, a new diffusion purification algorithm that is now the state-of-the-art purification method against several adversarial attacks.
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality.
Ranked #11 on
Image Generation
on ImageNet 512x512
Training Energy-Based Models with Diffusion Contrastive Divergences
In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs.
Entropy-based Training Methods for Scalable Neural Implicit Sampler
These benchmarks include sampling from 2D targets, Bayesian inference, and sampling from high-dimensional energy-based models (EBMs).
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models.
A Comprehensive Survey on Knowledge Distillation of Diffusion Models
Our objective is to provide a comprehensible overview of the modern approaches for distilling DMs, starting with an introduction to DMs and a discussion of the challenges involved in distilling them into neural vector fields.
A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization
We also apply BLiE to search for noise schedule of diffusion models.
ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso
A vital problem in solving classification or regression problem is to apply feature engineering and variable selection on data before fed into models. One of a most popular feature engineering method is to discretisize continous variable with some cutting points, which is refered to as bining processing. Good cutting points are important for improving model's ability, because wonderful bining may ignore some noisy variance in continous variable range and keep useful leveled information with good ordered encodings. However, to our best knowledge a majority of cutting point selection is done via researchers domain knownledge or some naive methods like equal-width cutting or equal-frequency cutting. In this paper we propose an end-to-end supervised cutting point selection method based on group and fused lasso along with the automatically variable selection effect. We name our method \textbf{ABM}(automatic bining machine).
