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Found 9 papers, 4 papers with code
Multi-Directional Subspace Editing in Style-Space
This paper describes a new technique for finding disentangled semantic directions in the latent space of StyleGAN.
DDNeRF: Depth Distribution Neural Radiance Fields
We present depth distribution neural radiance field (DDNeRF), a new method that significantly increases sampling efficiency along rays during training while achieving superior results for a given sampling budget.
DeepShadow: Neural Shape from Shadow
We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals.
Pairwise Margin Maximization for Deep Neural Networks
The weight decay regularization term is widely used during training to constrain expressivity, avoid overfitting, and improve generalization.
Margin-Based Regularization and Selective Sampling in Deep Neural Networks
We derive a new margin-based regularization formulation, termed multi-margin regularization (MMR), for deep neural networks (DNNs).
Autoencoder Image Interpolation by Shaping the Latent Space
We argue that these incongruities are due to the structure of the latent space and because such naively interpolated latent vectors deviate from the data manifold.
Proximity Preserving Binary Code using Signed Graph-Cut
We show that the proposed approximation is superior to the commonly used spectral methods with respect to both accuracy and complexity.
Selective sampling for accelerating training of deep neural networks
We present a selective sampling method designed to accelerate the training of deep neural networks.
The Generalized Laplacian Distance and Its Applications for Visual Matching
The graph Laplacian operator, which originated in spectral graph theory, is commonly used for learning applications such as spectral clustering and embedding.
