6 天前 - … models, we perform data augmentation to increase the number … transformation
techniques during augmentation. In this … 7.19 shows the training graph Faster-RCNN on our data. …

7 天前 - … For the training set, use random horizontal flips, random rotations, random blurring,
and random scaling operations to achieve fivefold data augmentation. The settings of the …

9 天前 - … -of-the-art deep learning-based methodologies applied … novel techniques for fossil
data augmentation and fossil image … networks, transformers, and graph neural networks, to …

9 天前 - … challenge by trajectory data augmentation methods which creates more RTF data
by … data, which is then used in a graph neural network for prognostics. Unlike conventional …

10 天前 - … complex graph structures and boost performance in large-scale graph learning
tasks [37]. … One of the key findings of our study is the effectiveness of data augmentation in …

11 天前 - … interpretability to this issue from a graph structure learning perspective, supported
by our … significant improvements over heuristic data augmentation methods and integrates …

21 天前 - … The Seq2Seq-LSTM model is augmented by a statistical model based on the
Bayes … of the data used, the hyperparametrization of models, the lack of graphical interfaces …

21 天前 - … survey, we developed three visualization views: the Encoder View unveils the process
of converting classical input data … designs, ie, satellite chart and augmented heatmap, are …

28 天前 - … In this paper, Knowledge Graphs (KG), based on graph theory fundamentals, storing
… Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:…

29 天前 - … other geometric transformations without explicit data augmentation, constructing a
hierarchical rotation-invariant descriptor from multilevel graph convolutional abstractions and …