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Found 6 papers, 3 papers with code
Dataset Distillation in Latent Space
Dataset distillation (DD) is a newly emerging research area aiming at alleviating the heavy computational load in training models on large datasets.
ControlCom: Controllable Image Composition using Diffusion Model
To address these challenges, we propose a controllable image composition method that unifies four tasks in one diffusion model: image blending, image harmonization, view synthesis, and generative composition.
WeditGAN: Few-Shot Image Generation via Latent Space Relocation
In this work, we introduce WeditGAN, which realizes model transfer by editing the intermediate latent codes $w$ in StyleGANs with learned constant offsets ($\Delta w$), discovering and constructing target latent spaces via simply relocating the distribution of source latent spaces.
Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection
We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss.
Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models
We reflect this nature with the confusion of a face identification model and measure the confusion with the maximum value of the output probability distribution.
Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation
First, we train a data-efficient StyleGAN2 on defect-free images as the backbone.
