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Found 13 papers, 3 papers with code
Feature Selection using the concept of Peafowl Mating in IDS
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources.
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks
To capture these dependencies, our approach incorporates a hybrid explicit-implicit tri-plane representation inspired by 3D-aware generative frameworks developed for three-dimensional object representation and employs a singular latent code to model an entire video sequence.
SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes
We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans.
Adversarial Likelihood Estimation With One-Way Flows
Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples.
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla
We also demonstrate the significance of domain selection while building a corpus by assessing these models on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark - BanSpeech, which contains approximately 6. 52 hours of human-annotated speech and 8085 utterances from 13 distinct domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Nonwatertight Mesh Reconstruction
Reconstructing 3D non-watertight mesh from an unoriented point cloud is an unexplored area in computer vision and computer graphics.
LED: Latent Variable-based Estimation of Density
Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data points and (2) those that provide exact density estimation, at the expense of sample quality and compactness of the latent space.
InvGAN: Invertible GANs
Our \textbf{InvGAN}, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model.
Populating 3D Scenes by Learning Human-Scene Interaction
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
Ranked #4 on
Contact Detection
on BEHAVE
GIF: Generative Interpretable Faces
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Our model has the form of a variational autoencoder, with a Gaussian mixture prior on the latent vector.
Learning Human Motion Models for Long-term Predictions
Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.
