MIC casts diffusion motion generation as stochastic control to support both objective-based and criterion-based constraints without training or differentiability requirements.
Available: http://www.jstor.org/stable/2240405
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
The method uses Smooth ℓ1 loss, divergence regularization, and input optimization in DIP to prevent overfitting and achieve better denoising on real HSIs with Gaussian, sparse, and stripe noise than prior DIP variants.
Proposes two affine models (Bussgang-based and SDR-maximizing) for SAR-ADC non-idealities and uses them to devise mitigation methods in massive MU-MIMO systems.
citing papers explorer
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Training-free Controllable Human Motion Generation under Heterogeneous Constraints
MIC casts diffusion motion generation as stochastic control to support both objective-based and criterion-based constraints without training or differentiability requirements.
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Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
The method uses Smooth ℓ1 loss, divergence regularization, and input optimization in DIP to prevent overfitting and achieve better denoising on real HSIs with Gaussian, sparse, and stripe noise than prior DIP variants.