Image Morphing
18 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
DiffMorph: Text-less Image Morphing with Diffusion Models
The image generation capability of our work is demonstrated through our results and a comparison of these with prompt-based image generation.
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation.
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks
Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential.
Vulnerability of 3D Face Recognition Systems to Morphing Attacks
A substantial amount of research is being done for generation of high quality face morphs along with detection of attacks from these morphs.
Measure transfer via stochastic slicing and matching
The proof builds on an interpretation as a stochastic gradient descent scheme on the Wasserstein space.
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
This paper highlights the addition of a sequential layer to the traditional RESNET 18 model for computing the accuracy of an Image classification dataset.
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
The accuracies are acquired for each augmentation technique using a RESNET18 model.
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
A painful element of real data is that it tends to be imbalanced.
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
We study the partitioning of the domain in feature space, identify regions guaranteed to have certain classifications, and investigate its implications for the pixel space.