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 Le
(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. ...
idea of transfer learning in the training step: a learning strategy widely used in deep learning to compensate for the lack of datasets (Kaplan Berkaya et al., 2021). The network is fine-tuned with a homemade cow leg dataset on models trained on publicly available large-scale tracking ...
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...
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 = ...
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...
However, most existing models lack the consideration of model efficiency, where they neither take into account the time and space complexity nor fully evaluate the efficiency. In particular, for deep learning methods, the huge overhead of memory and runtime in the complex neural network leads to...
In this work, we trained deep learning models to classify epithelial tumours in biopsy WSIs of stomach and colon. Each model was trained on WSI obtained from a single medical institution and was evaluated on two independent test sets originating from the same and from a different institution, re...
CNN-Based Models(基于卷积神经网络) RNN通常用于捕捉序列中的依赖信息,而CNN在图像领域应用较多的原因是图像通常是多维表示(相对于文本一维形式),相邻的图像像素之间关系密切,不同区域的特征刻画不同的图像特征,通过卷积可以捕捉到不同空间上的特征。有许多研究人员将CNN引入到NLP领域,利用 CNN可以有效的捕捉局部信息,...
Deep learning models have shown to achieve high performance in encrypted traffic classification. However, when it comes to production use, multiple factors challenge the performance of these models. The emergence of new protocols, especially at the application layer, as well as updates to previous pr...