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. I
The Essential Guide to Data Augmentation in Deep Learning Video annotation AI video annotation Get started today Explore V7 Darwin Pragati Baheti Pragati is a software developer at Microsoft, and a deep learning enthusiast. She writes about the fundamental mathematics behind deep neural networks. ...
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...
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...
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...
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 ...
A critical aspect of AI agents is their capacity for learning and adaptation. Through the integration of technologies such as Large Language Models, they continuously improve their performance based on interactions, evolving into more sophisticated and intelligent assistants over time. In case of Autonom...