arXiv subjects

Insaf Ashrapov

Publications and source records attributed to Insaf Ashrapov.

3 recordsLinked to original sources

arXiv2022

Automatic satellite building construction monitoring

One of the promising applications of satellite images is building construction monitoring. It allows to control the construction progress around the world even in the locations that are hard to reach. One of the main hurdles of this approach is the interpretation of the image data. In this paper, we have employed several novel deep learning techniques to tackle the problem. Various image segmentation and object detection networks were combined into a unified pipeline, which was then used to determine the building construction progress.

cs.CV↗

arXiv2020

Tabular GANs for uneven distribution

Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular data generation, recent advances in diffusion models and large language models (LLMs) have opened new paradigms with complementary strengths in sample quality, privacy, and controllability. In this paper, we survey the landscape of tabular data generation across three major paradigms - GANs, diffusion models, and LLMs - and introduce a unified, modular framework that supports all three. The framework encompasses data preprocessing, a model-agnostic interface layer, standardized training and inference pipelines, and a comprehensive evaluation module. We validate the framework through experiments on seven benchmark datasets, demonstrating that GAN-based augmentation can improve downstream performance under distribution shift. The framework and its reference implementation are publicly available at https://github.com/Diyago/Tabular-data-generation, facilitating reproducibility and extensibility for future research.

cs.LG↗

arXiv2018

Automatic salt deposits segmentation: A deep learning approach

One of the most important applications of seismic reflection is the hydrocarbon exploration which is closely related to salt deposits analysis. This problem is very important even nowadays due to it's non-linear nature. Taking into account the recent developments in deep learning networks TGS-NOPEC Geophysical Company hosted the Kaggle competition for salt deposits segmentation problem in seismic image data. In this paper, we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place (top 1%) in the mentioned competition. Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as our base architecture, we implemented Spatial-Channel Squeeze & Excitation, Lovasz loss, CoordConv and Hypercolumn methods. The source code for our solution is made publicly available at https://github.com/K-Mike/Automatic-salt-deposits-segmentation.

cs.LG↗