[HTML][HTML] A survey on generative adversarial networks for imbalance problems in computer vision tasks
Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …
preprocessing and pattern recognition steps to perform a task. When the acquired images …
Cogview2: Faster and better text-to-image generation via hierarchical transformers
Abstract Development of transformer-based text-to-image models is impeded by its slow
generation and complexity, for high-resolution images. In this work, we put forward a …
generation and complexity, for high-resolution images. In this work, we put forward a …
Efficiently modeling long sequences with structured state spaces
A central goal of sequence modeling is designing a single principled model that can
address sequence data across a range of modalities and tasks, particularly on long-range …
address sequence data across a range of modalities and tasks, particularly on long-range …
Generating diverse structure for image inpainting with hierarchical VQ-VAE
Given an incomplete image without additional constraint, image inpainting natively allows
for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting …
for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting …
A holistic review of machine learning adversarial attacks in IoT networks
With the rapid advancements and notable achievements across various application
domains, Machine Learning (ML) has become a vital element within the Internet of Things …
domains, Machine Learning (ML) has become a vital element within the Internet of Things …
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Adversarial perturbations of normal images are usually imperceptible to humans, but they
can seriously confuse state-of-the-art machine learning models. What makes them so …
can seriously confuse state-of-the-art machine learning models. What makes them so …
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Flow-based generative models are powerful exact likelihood models with efficient sampling
and inference. Despite their computational efficiency, flow-based models generally have …
and inference. Despite their computational efficiency, flow-based models generally have …
Conditional time series forecasting with convolutional neural networks
We present a method for conditional time series forecasting based on an adaptation of the
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Artificial neural networks were recently shown to be an efficient representation of highly
entangled many-body quantum states. In practical applications, neural-network states inherit …
entangled many-body quantum states. In practical applications, neural-network states inherit …
Pointgrow: Autoregressively learned point cloud generation with self-attention
Generating 3D point clouds is challenging yet highly desired. This work presents a novel
autoregressive model, PointGrow, which can generate diverse and realistic point cloud …
autoregressive model, PointGrow, which can generate diverse and realistic point cloud …