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OnAnalyzingGenerativeandDenoisingCapabilities ofDiffusion-basedDeepGenerativeModels
While the performance of DDGMs is impressive, not all of their aspects are fully understood.
fb2697869f56484404c8ceee2985b01d-Supplemental.pdf
Weshowthat thiscansuccessfully yield "one-shot" knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d.
ACommunication-Efficient Distributed Gradient Clipping Algorithmfor Training Deep Neural Networks
In accelerate restricted 51, 50].
High-dimensional (Group) Adversarial Training in Linear Regression
In this paper, we will give the non-asymptotic convergence rate of the prediction error in high-dimensional adversarial training.
LightweightGenerativeAdversarialNetworksfor Text-GuidedImageManipulation
Methods introduced in [4, 19] both fail toeffectively modify text-required attributes, and results are also farfrom satisfactory (see Figure 1).
