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Found 4 papers, 2 papers with code
MACE: Mass Concept Erasure in Diffusion Models
In this paper, we introduce MACE, a finetuning framework for the task of mass concept erasure.
TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition
In this paper, we propose TF-ICON, a novel Training-Free Image COmpositioN framework that harnesses the power of text-driven diffusion models for cross-domain image-guided composition.
Probabilistic Deep Ordinal Regression Based on Gaussian Processes
Based on that, it proposes a deep neural network with a GPs layer on the top, which is trained end-to-end by the stochastic gradient descent method for both neural network parameters and GPs parameters.
Ranked #4 on
Age Estimation
on Adience
A Constrained Deep Neural Network for Ordinal Regression
An implementation based on the CNN framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method.
Ranked #3 on
Historical Color Image Dating
on HCI
