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Found 8 papers, 2 papers with code
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models.
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.
Ranked #1 on
Satellite Image Classification
on SAT-4
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters
Classification techniques for images of handwritten characters are susceptible to noise.
Ranked #1 on
Document Image Classification
on n-MNIST
CactusNets: Layer Applicability as a Metric for Transfer Learning
Deep neural networks trained over large datasets learn features that are both generic to the whole dataset, and specific to individual classes in the dataset.
Core Sampling Framework for Pixel Classification
The intermediate map responses of a Convolutional Neural Network (CNN) contain information about an image that can be used to extract contextual knowledge about it.
A Theoretical Analysis of Deep Neural Networks for Texture Classification
To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate.
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem.
DeepSat - A Learning framework for Satellite Imagery
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.
Ranked #2 on
Satellite Image Classification
on SAT-6
