Lightweight Image Super-Resolution with Enhanced CNN (Knowledge-Based Systems,2020)
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Updated
Oct 12, 2022 - Python
Lightweight Image Super-Resolution with Enhanced CNN (Knowledge-Based Systems,2020)
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Implementation of MobileNetV3 in pytorch
This repository contains the architectures, Models, logs, etc pertaining to the SimpleNet Paper (Lets keep it simple: Using simple architectures to outperform deeper architectures )
Asymmetric CNN for image super-resolution (IEEE Transactions on Systmes, Man, and Cybernetics: Systems 2021)
Coarse-to-Fine CNN for Image Super-Resolution (IEEE Transactions on Multimedia,2021)
In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.
Facial analysis framework for genetic disorders with facial dysmorphism
Enhanced CNN for image denoising (CAAI Transactions on Intelligence Technology, 2019)
Attention-guided CNN for image denoising(Neural Networks,2020)
Designing and Training of A Dual CNN for Image Denoising (Knowledge-based Systems, 2021)
Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)
1D convolutional neural networks for activity recognition in python.
sentiment-analysis,document-classification,svm,logistic-regression,rcnn-text-classification, cnn-text-classification, lstm-text-classification, naive-bayes-classifier, sklearn-classify, pytorch
Class to automatic create Convolutional Neural Network in PyTorch
Dock2D: Synthetic datasets for the molecular recognition problem
Deformation-invariant line-level Handwritten Text Recognition (HTR) using a convolutional-only architecture.
Repository consists of pre-trained CNN model in pytorch, hitting 89% on Fashion MNIST dataset. Adversarial attack was implemented on a given model. Results are below.
Computer Vision in Python
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