This project utilizes three deep learning models, DNN, CNN, and LSTM, for the classification and recognition of 11 modulation types, including BPSK, QPSK, 8PSK, 16QAM, 64QAM, PAM4, GFSK, CPFSK, B-FM, DSB-AM, and SSB-AM, under varying signal-to-noise ratio conditions. ...
Deep learning (DL) models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low ...
check here for formal report of large scale multi-label text classification with deep learning several models here can also be used for modelling question answering (with or without context), or to do sequences generating. we explore two seq2seq model(seq2seq with attention,transformer-attention...
Therefore, the deep learning models’ memory and computational efficiency are critical for deployment. Lower memory use and higher efficiency are essential in these environments. This study compares various deep learning models’ memory consumption and inference performance to guide model selection for ...
(tensorflow 1.1 to 1.13 should also works; most of models should also work fine in other tensorflow version, since we use very few features bond to certain version. if you use python3, it will be fine as long as you change print/try catch function in case you meet any error. ...
3.基于CNN的模型训练识别text的模式,像是关键短语在文本分类任务上(CNN-based models are trained to recognize patterns in text, such as key phrases, for TC) RNN设计识别与时间相关,而cnn进行跨空间的识别。 4.Capsule networks address the information loss problem suffered by the pooling operations of CN...
mobilenet_v3(inverted_residual_setting=inverted_residual_setting, last_channels=last_channels, block=None, norm_layer=None, num_classes=num_classes, init_weights=init_weights) if pretrained: # if you want to use cpu, you should modify map_loaction=torch.device("cpu") pretrained_models = ...
A CNN is a powerful machine learning technique from the field of deep learning. CNNs are trained using large collections of diverse images. From these large collections, CNNs can learn rich feature representations for a wide range of images. These feature representations often outperform hand-craft...
Pollin, "Distributed deep learning models for wireless signal classification with low-cost spectrum sensors," arXiv preprint arXiv:1707.08908, 2017.S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S. Pollin, "Distributed ... S Rajendran,W Meert,D Giustiniano,... 被引量: 0发表...
During training of deep learning models for segmentation, the parameters of the model are optimized by minimizing the difference, encoded by the loss function, between the model’s predictions and the real label from the reference standard. Therefore, the selection of an effective loss function to...