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MovieLens-25M
The dataset used in the Controllable Gradient Item Retrieval paper, which consists of user-item interaction data and item-attribute relation data. -
Simulated Standard Model events
The dataset used for training and testing the β-parameterized Variational Autoencoder (VAE) for event compression in particle physics experiments. -
Variational learning across domains with triplet information
The paper proposes the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains with the help of the learning triplets... -
Learning a Representation Map for Robot Navigation using Deep Variational Aut...
The dataset used in this paper is a video of a house tour, which is used to train a Variational Autoencoder (VAE) to learn a representation map for robot navigation. -
Generative time series models using Neural ODE in Variational Autoencoders
The dataset consists of time series data in the form of spring oscillations, solar power production data, and spiral data. -
Synthetic Data Generation for Variational Autoencoders
Synthetic swaption cubes generated from existing ones -
Interpolation of Missing Swaption Volatility Data using Variational Autoencoders
Daily (Bachelier model implied) swaption volatility cubes of European LIBOR swaptions -
VOLTA: Improving Generative Diversity by Variational Mutual Information Maxim...
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used six datasets from three different NLG tasks. -
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. The proposed method learns sparse data... -
Variational Grid Setting Network
Variational Grid Setting Network (VGSN) for generating missing Chinese characters from a Chinese font set. -
BBRL Activations Dataset
The dataset used in the paper is a collection of activations from a feature extraction network and a reactive network, used to train a Variational Autoencoder (VAE) to learn... -
FusionT-LESS
Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior... -
FusionCelebA
Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior... -
FusionMNIST
Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior...
