[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review
Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
Normalization techniques in training dnns: Methodology, analysis and application
Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …
generalization of deep neural networks (DNNs), and have successfully been used in various …
The non-iid data quagmire of decentralized machine learning
Many large-scale machine learning (ML) applications need to perform decentralized
learning over datasets generated at different devices and locations. Such datasets pose a …
learning over datasets generated at different devices and locations. Such datasets pose a …
Evolution of image segmentation using deep convolutional neural network: A survey
From the autonomous car driving to medical diagnosis, the requirement of the task of image
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …
Understanding the generalization benefit of normalization layers: Sharpness reduction
Abstract Normalization layers (eg, Batch Normalization, Layer Normalization) were
introduced to help with optimization difficulties in very deep nets, but they clearly also help …
introduced to help with optimization difficulties in very deep nets, but they clearly also help …
Understanding and improving layer normalization
Layer normalization (LayerNorm) is a technique to normalize the distributions of
intermediate layers. It enables smoother gradients, faster training, and better generalization …
intermediate layers. It enables smoother gradients, faster training, and better generalization …
Sgd with large step sizes learns sparse features
M Andriushchenko, AV Varre… - International …, 2023 - proceedings.mlr.press
We showcase important features of the dynamics of the Stochastic Gradient Descent (SGD)
in the training of neural networks. We present empirical observations that commonly used …
in the training of neural networks. We present empirical observations that commonly used …
A lightweight CNN for Diabetic Retinopathy classification from fundus images
Diabetic Retinopathy (DR) is a complication of diabetes mellitus that damages blood vessel
networks in the retina. This is a serious vision-threatening issue in most diabetic subjects …
networks in the retina. This is a serious vision-threatening issue in most diabetic subjects …
Transferable adversarial training: A general approach to adapting deep classifiers
Abstract Domain adaptation enables knowledge transfer from a labeled source domain to an
unlabeled target domain. A mainstream approach is adversarial feature adaptation, which …
unlabeled target domain. A mainstream approach is adversarial feature adaptation, which …
A survey on green deep learning
In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …