Search Results for author:
Found 12 papers, 1 papers with code
LSTM Framework for Classification of Radar and Communications Signals
Therefore, we propose DeepRadar2022, a radar dataset used in our systematic evaluations that is available publicly and will facilitate a standard comparison between methods.
Semi-supervised Domain Adaptation for Semantic Segmentation
We demonstrate that the proposed approach outperforms state-of-the-art methods on two common synthetic-to-real semantic segmentation benchmarks.
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation
Training a CNN to perform semantic segmentation requires a large amount of labeled data, where the production of such labeled data is both costly and labor intensive.
Domain Adaptation on Semantic Segmentation for Aerial Images
In this paper, we propose a novel unsupervised domain adaptation framework to address domain shift in the context of aerial semantic image segmentation.
Accelerated WGAN update strategy with loss change rate balancing
Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result in overfitting.
Generating Image Sequence from Description with LSTM Conditional GAN
Generating images from word descriptions is a challenging task.
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask
We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.
Lecture video indexing using boosted margin maximizing neural networks
This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system.
CGMOS: Certainty Guided Minority OverSampling
In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
In this paper, we investigate and formalize a general framework-Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently.
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.
