Incrementally learning new information from a non-stationary stream of data, referred to as ‘continual learning’, is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed,...
A Step-by-Step Guide to Text Annotation [+Free OCR Tool] The Essential Guide to Data Augmentation in Deep Learning Pragati is a software developer at Microsoft, and a deep learning enthusiast. She writes about the fundamental mathematics behind deep neural networks. Up next...
The proposed method is a combination of data augmentation techniques and a regularization method known as “adversarial training” to improve the robustness of deep learning-based malware detectors. The authors of the paper evaluated the effectiveness of their proposed method using a dataset of malware...
An AI model is a computer program trained to identify patterns in data. AI stands for “artificial intelligence,” and such models are built to mimic the powers of human intelligence. This is made possible through a mix of machine learning (ML), deep learning, natural language processing (NLP...
The Effectiveness of Data Augmentation in Image Classification using Deep Learning: Tech. rep. Stanford University (2017) Google Scholar [49] Gu S., Pednekar M., Slater R. Improve image classification using data augmentation and neural networks SMU Data Sci. Rev., 2 (2) (2019), pp. 1-43...
Due to class imbalance in the PAF and PsAF classes, data augmentation techniques were utilized to increase the number of PAF and PsAF images to match the count of Non-AF images. The training, validation, and testing ratios were 0.7, 0.15, and 0.15, respectively. The training set consisted...
Copy the contents of this TensorList to an external pointer (of type ctypes.c_void_p) residing in CPU memory. This function is used internally by plugins to interface with tensors from supported Deep Learning frameworks.data_ptr(self: TensorListCPU) → objectReturns the address of the first...
the process includes the application of data augmentation and different preprocessing techniques to enhance performance. Accuracy, precision, recall, F1 score, and confusion matrix are on show to conduct an evaluation. Moreover, GradCam and guided GradCam are applied to analyze the model’s performan...
In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the ...
suffered a significant performance drop (Pearsonr = 0.6; Extended Data Fig.2d). GET also outperformed simpler machine learning approaches in both the leave-out cell type and leave-out chromosome evaluation settings when trained using the same data and number of epochs (Extended Data Fig.2e,...