Search Results for author:
Found 9 papers, 6 papers with code
Reconstructing Training Data from Multiclass Neural Networks
Reconstructing samples from the training set of trained neural networks is a major privacy concern.
SinFusion: Training Diffusion Models on a Single Image or Video
Our image/video-specific diffusion model (SinFusion) learns the appearance and dynamics of the single image or video, while utilizing the conditioning capabilities of diffusion models.
Reconstructing Training Data from Trained Neural Networks
We propose a novel reconstruction scheme that stems from recent theoretical results about the implicit bias in training neural networks with gradient-based methods.
Diverse Video Generation from a Single Video
GANs are able to perform generation and manipulation tasks, trained on a single video.
Diverse Generation from a Single Video Made Possible
GANs are able to perform generation and manipulation tasks, trained on a single video.
From Discrete to Continuous Convolution Layers
We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.
Implicit Geometric Regularization for Learning Shapes
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks.
Controlling Neural Level Sets
In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.
Surface Networks via General Covers
Specifically, for the use case of learning spherical signals, our representation provides a low distortion alternative to several popular spherical parameterizations used in deep learning.
