Web Design
Autoencoders with Grad-CAM
Implemented a deep learning project using autoencoders and Grad-CAM (Gradient-weighted Class Activation Mapping) for image analysis. Utilized autoencoders for unsupervised feature learning and image reconstruction, while integrating Grad-CAM for visualizing and interpreting model predictions.
Year :
2024
Project Duration :
8 weeks
Technologies:
Deep Learning · Image Processing · Python (Programming Language)
Project :
Traditional image analysis pipelines using autoencoders offer limited interpretability, making it difficult to understand which features influence reconstructions or anomaly detection decisions.
Developed a deep learning framework integrating autoencoders for unsupervised image reconstruction with Grad-CAM to highlight influential image regions. This helped visualize the model’s focus areas during encoding and decoding.
Challenge :
Adapting Grad-CAM to work with unsupervised architectures required modifying intermediate layers to support class-agnostic visual explanations.
Summary :
Combined unsupervised learning and visual interpretability for improved transparency in image-based deep learning tasks.
More Projects
Web Design
Autoencoders with Grad-CAM
Implemented a deep learning project using autoencoders and Grad-CAM (Gradient-weighted Class Activation Mapping) for image analysis. Utilized autoencoders for unsupervised feature learning and image reconstruction, while integrating Grad-CAM for visualizing and interpreting model predictions.
Year :
2024
Project Duration :
8 weeks
Technologies:
Deep Learning · Image Processing · Python (Programming Language)
Project :
Traditional image analysis pipelines using autoencoders offer limited interpretability, making it difficult to understand which features influence reconstructions or anomaly detection decisions.
Developed a deep learning framework integrating autoencoders for unsupervised image reconstruction with Grad-CAM to highlight influential image regions. This helped visualize the model’s focus areas during encoding and decoding.
Challenge :
Adapting Grad-CAM to work with unsupervised architectures required modifying intermediate layers to support class-agnostic visual explanations.
Summary :
Combined unsupervised learning and visual interpretability for improved transparency in image-based deep learning tasks.
More Projects
Web Design
Autoencoders with Grad-CAM
Implemented a deep learning project using autoencoders and Grad-CAM (Gradient-weighted Class Activation Mapping) for image analysis. Utilized autoencoders for unsupervised feature learning and image reconstruction, while integrating Grad-CAM for visualizing and interpreting model predictions.
Year :
2024
Project Duration :
8 weeks
Technologies:
Deep Learning · Image Processing · Python (Programming Language)
Project :
Traditional image analysis pipelines using autoencoders offer limited interpretability, making it difficult to understand which features influence reconstructions or anomaly detection decisions.
Developed a deep learning framework integrating autoencoders for unsupervised image reconstruction with Grad-CAM to highlight influential image regions. This helped visualize the model’s focus areas during encoding and decoding.
Challenge :
Adapting Grad-CAM to work with unsupervised architectures required modifying intermediate layers to support class-agnostic visual explanations.
Summary :
Combined unsupervised learning and visual interpretability for improved transparency in image-based deep learning tasks.