Determining which images have the greatest positive and negative impact on a Deep Neural Network model’s predictions. Link to Github repository Using PyTorch to Identify Model Influences This project is based on the nimarb reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang. The reference implementation can be found here: link. The Dataset The project makes use of the CIFAR-10 image dataset, which consists of 50,000 training images and 10000 test images in 10 classes.