What are some techniques for few-shot learning and meta-learning in the context of CNNs?

Aug 1, 2019 ... ... CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object ... Jan 22, 2025 ... A breakthrough in building models for image classification came with the discovery that a convolutional neural network (CNN) could be used ... Jan 23, 2023 ... Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN ... CNN is one of the popular deep learning algorithms that recently gained popularity in the sector of hydrology and earth sciences (Panahi et al., 2020). The CNN ... This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. Complex problems can be solved and objects can be recognized in images based on the Google TensorFlow ™ library - a widely used deep learning framework. Based ... We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not ... Jun 27, 2024 ... ... machine learning by regression-based CNN. As a result, the lowest nonlinearity of 0.3% with precision as high as 98.7 is given in our PSD as ... In this paper, three prediction algorithms have been proposed which will predict the protein secondary structure based on machine learning.