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Found 7 papers, 2 papers with code
Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights
In LoRAE, we incorporated a low-rank regularizer to adaptively reconstruct a low-dimensional latent space while preserving the basic objective of an autoencoder.
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition
Although researchers have been studying multiple ways to generate code comments automatically, previous work mainly considers representing a code token in its entirety semantics form only (e. g., a language model is used to learn the semantics of a code token), and additional code properties such as the tree structure of a code are included as an auxiliary input to the model.
An Exploratory Study on Code Attention in BERT
When identifiers' embeddings are used in CodeBERT, a code-based PLM, the performance is improved by 21-24% in the F1-score of clone detection.
Salient Image Matting
This is done by employing a salient object detection model to produce a trimap of the most salient object in the image in order to guide the matting model about higher-level object semantics.
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations
However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input.
AlphaNet: An Attention Guided Deep Network for Automatic Image Matting
To achieve complete automatic foreground extraction in natural scenes, we propose a method that assimilates semantic segmentation and deep image matting processes into a single network to generate detailed semantic mattes for image composition task.
Retrieving Similar E-Commerce Images Using Deep Learning
In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity.
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