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Found 20 papers, 6 papers with code
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
Zero-Shot Learning via Class-Conditioned Deep Generative Models
We present a deep generative model for learning to predict classes not seen at training time.
Adversarial Symmetric Variational Autoencoder
A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data.
Triangle Generative Adversarial Networks
The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.
Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence.
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching.
Continuous-Time Flows for Efficient Inference and Density Estimation
Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees.
VAE Learning via Stein Variational Gradient Descent
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent.
Compressive Sensing via Convolutional Factor Analysis
During reconstruction and testing, we project the upper layer dictionary to the data level and only a single layer deconvolution is required.
Adaptive DCTNet for Audio Signal Classification
Its output feature is related to Cohen's class of time-frequency distributions.
Sound
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
A multi-way factor analysis model is introduced for tensor-variate data of any order.
Semantic Compositional Networks for Visual Captioning
The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Recurrent neural networks (RNNs) have shown promising performance for language modeling.
Adaptive Feature Abstraction for Translating Video to Text
Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features.
Learning Generic Sentence Representations Using Convolutional Neural Networks
We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes.
Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as associated labels or captions.
Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification
Learning the representation of shape cues in 2D & 3D objects for recognition is a fundamental task in computer vision.
A Deep Generative Deconvolutional Image Model
A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework.
A Generative Model for Deep Convolutional Learning
A generative model is developed for deep (multi-layered) convolutional dictionary learning.
Generative Deep Deconvolutional Learning
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning.
