Search Results for author:
Found 9 papers, 4 papers with code
Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation
Our experiments on imbalanced image dataset classification show that, the validation accuracy improvement with such re-balancing method is related to the image similarity between different classes.
Network-Free, Unsupervised Semantic Segmentation With Synthetic Images
We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training.
When do GANs replicate? On the choice of dataset size
With empirical evidence from BigGAN and StyleGAN2, on datasets CelebA, Flower and LSUN-bedroom, we show that dataset size and its complexity play an important role in GANs replication and perceptual quality of the generated images.
Near Perfect GAN Inversion
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it.
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features
We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant.
Diamond in the rough: Improving image realism by traversing the GAN latent space
Leveraging the learned structure of the latent space, we find moving in this direction corrects many image artifacts and brings the image into greater realism.
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent
In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship.
Computing the Testing Error without a Testing Set
Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset.
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image
Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms.
