Explainable Neural Networks: Recent Advancements, Part 1 Explainable Neural Networks: Recent Advancements, Part 2 Explainable Neural Networks: Recent Advancements, Part 3 Network In Network by Min Lin 2013 Gradient Based Approaches Visualizing Gradients (2014) In the paper “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps“ Karen Simonyan et. al. proposed two visualisation …
Books Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar Papers Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning” 2017.
Domain Randomization: future of robust modeling by Urwa Muaz Invariance, Causality, and Robust Deep Learning – Why do Neural networks fail to generalize to new environments, and how can this be fixed? by by Urwa Muaz
Details about the usecase can be studied from the references given below in the references section. Following is the source code. The code was run on Google Colab. Imports & Utilities Importing the Libraries Following is the code to set device based on the type of machine (CPU or GPU). Following is the code for …
Chromosome Segmentation in Pytorch using U-Net Architecture Read More »
Neural Architecture Search Neural Architecture Search: A Survey – Thomas Elsken Literature on Neural Architecture Search Transfer Learning & Domain Adaptation Model Compression – Cristian Bucila˘ et. al. – KDD 2006 A Survey on Transfer Learning – Sinno Jialin Pan, et. al. – 2010 Unbiased look at dataset bias – Antonio Torralba et. al. – …
Important Links Face Recognition: Understanding LBPH Algorithm – Link
