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 Baheti Pragati is a software developer at Microsoft, and a deep learning enthusiast. She writes about the fundamental mathematics behind deep neural networks....
An important open problem in deep learning is enabling neural net- works to incrementally learn from non-stationary streams of data1,2. For example, when deep neural networks are trained on samples from a new task or data distribution, they tend to rapidly lose previously acquired capabilities,...
In this study, we introduce a novel framework named Supervised Contrastive Learning for Single Cell (SCLSC) for single-cell type annotation. SCLSC method consists of two steps. First, SCLSC leverages supervised contrastive learning to learn a better data representation that captures both class discr...
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: nvidia.dali.backend_impl.TensorListCPU)→ object¶ Retu...
azdata azure-function-log-intercept azure-mobile-services-client azure-sb b-spline b2a b4a b64-lite b_ babar babel-code-frame babel-core babel-generator babel-plugin-glaze babel-plugin-macros babel-plugin-react-docgen babel-plugin-react-html-attrs babel-plugin-react-pug babel-plugin-syntax-jsx ...
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 role of synthetic data in AI model Data Augmentation:Synthetic data is your secret weapon for increasing the performance of your AI model. By creating more artificially generated data that matches your real data, your AI models will have more examples to train from, improving generalization an...
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...
Data augmentationis basically the process of adding to the data set slightly modified copies of existing elements. Applying data augmentation to our data set we would expand it with almost the same faces but with a bit of difference in eye color or skin tone. ...