DEELE-Rad: Deep Learning-based Radiomics

Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization

Deep Learning Breast MRI Segmentation and Classification

Fork of the Mario Kart 64 Gym Environment. Includes training scripts for RL algorithms and Grad-CAM visualization

Detection and localization of COVID-19 on chest X-rays

Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation

Gradient Frequency Attention: Tell Neural Networks where speaker information is.

Gradient Class Activation Map (with pytorch): Visualize the model's prediction to help understand CNN and ViT models better

Thesis name: Comparative evaluation of deep neural networks for automatic detection of violence scenes within videos

Image classification using deep learning models with activation map visualisation and TensorRT support

Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset

KL severity grading using SE-ResNet and SE-DenseNet architectures trained with Cross Entropy loss and Focal Loss. The hyperparameters of focal loss have been fine-tuned as well. Further, Grad-CAM has been implemented for visualization purposes.

Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.

Have you ever asked yourself, which regions of the input image were considered more by the model? If so, Grad-CAM has exciting answers for you!

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet

Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet

This study tries to compare the detection of lung diseases using xray scans from three different datasets using three different neural network architectures using Pytorch and perform an ablation study by changing learning rates. The dimensional understanding is visualised using t-SNE and Grad-CAM for visualisation of diseases in x-ray scans.

Repository of the course project of CMU 16-824 Visual Learning and Recognition

Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques