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Annotated Web Ears (AWE) dataset
The AWE dataset is a recent dataset of ear images gathered from the web with the goal of studying unconstrained ear recognition technology and is in our opinion the most... -
CHAOS dynamic dataset
The CHAOS dynamic dataset is an artificially created dynamic dataset from the CHAOS abdominal benchmark dataset. -
CHAOS dataset
The CHAOS dataset is an abdominal benchmark dataset comprising 80 volumes (40 subjects, in-phase and opposed-phase for each subject). -
Text-to-Mesh
Text-to-Mesh is a dataset of 3D models generated from text prompts. -
Goal Driven Discovery of Distributional Differences via Language Descriptions
Describing differences between text distributions with natural language. -
Learning a probabilistic latent space of object shapes via 3D generative-adve...
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling -
LION: Latent Point Diffusion Models for 3D Shape Generation
LION: Latent Point Diffusion Models for 3D Shape Generation -
COCO validation dataset
COCO validation dataset -
Dynamical Variational Autoencoders: A Comprehensive Review
A comprehensive review of dynamical variational autoencoders -
Auto-encoding variational Bayes
Auto-encoding variational Bayes -
ASVspoof 2021 LA
The ASVspoof 2021 LA dataset is used for testing the generalization capabilities of our model. -
ASVspoof 2019 LA-Degraded
The ASVspoof 2019 LA-Degraded dataset is constructed to test the performance of our model under different settings of bit-rate, Loss, DTX, and µ/a − law parameters for various... -
ASVspoof 2019 LA
The ASVspoof 2019 LA dataset encompasses three types of speaker representation: d-vector, one-hot embedding, and VAE. -
AMiRo dataset
The dataset used in the paper is a comprehensive three-class example, where each class is represented by a set of sensor modalities (camera and LiDAR). The dataset is used to... -
NESTA: A Specialized Neural Processing Engine for Efficient Convolutional Neu...
NESTA: a specialized neural processing engine designed for executing learning models in which filter-weights, input-data, and applied biases are expressed in fixed-point format. -
Low Precision Deep Learning Operators
The dataset used in this paper for low precision deep learning operators.
