1606.06565v1.pdf

9/14/2017

This link leads to a PDF titled "Deep Residual Learning for Image Recognition," which is a research paper published on arXiv. The paper introduces a new architecture called ResNet (Residual Network) that significantly improves the accuracy of image recognition tasks. It discusses the challenges faced by traditional deep neural networks in training very deep models and proposes the use of residual connections to address these issues. The authors provide experimental results that demonstrate the superior performance of ResNet compared to previous state-of-the-art models on various image datasets. The paper concludes by highlighting the potential of ResNet in advancing the field of computer vision.

feature extraction ꞏ pattern recognition ꞏ dimensionality reduction ꞏ computer vision ꞏ statistical learning ꞏ optimization algorithms ꞏ deep generative models ꞏ text classification ꞏ deep unsupervised learning ꞏ data mining ꞏ deep belief networks ꞏ natural language processing ꞏ convolutional neural networks ꞏ autoencoders ꞏ deep learning ꞏ deep reinforcement learning ꞏ model selection ꞏ supervised learning ꞏ neural networks ꞏ big data ꞏ reinforcement learning ꞏ unsupervised learning ꞏ regularization ꞏ artificial intelligence ꞏ machine learning ꞏ deep learning frameworks ꞏ neural network architectures ꞏ ensemble methods ꞏ deep learning applications ꞏ recurrent neural networks ꞏ image classification ꞏ deep neural networks ꞏ probabilistic models ꞏ generative models ꞏ transfer learning