Can interpretability help us to identify and mitigate biases in CNNs?

Current approaches to object recognition make essential use of machine learning methods. To im- prove their performance, we can collect larger datasets, learn ... Dec 7, 2017 ... While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to ... May 12, 2022 ... Comments94 · Day 1- Live Deep Learning Community Session · Day 3- Optimizers And ANN Implementation| Live Deep Learning Community Session. May 12, 2022 ... Enroll for free in the below link to get all the videos and materials https://ineuron.ai/course/Deep-Learning-Foundations Live Deep Learning ... Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ... Aug 15, 2022 ... What neural network is appropriate for your predictive modeling problem? It can be difficult for a beginner to the field of deep learning to ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. Layer-Wise Data-Free CNN Compression ... We present an efficient method for compressing a trained neural network without using any data. Our data-free method ... ... CNN deep feature extraction with machine learning models to classify variety of food image datasets. Date of Award, Oct 2018. Original language, English. Jul 19, 2021 ... To learn how to train your first CNN with PyTorch, just keep reading. CNNs are a type of deep learning algorithm that can analyze and extract ...