Search Results for author: Rishab Sharma

Found 7 papers, 1 papers with code

Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights

no code implementations24 Oct 2023 Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, Punit Rathore

In LoRAE, we incorporated a low-rank regularizer to adaptively reconstruct a low-dimensional latent space while preserving the basic objective of an autoencoder.

Image Generation

LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition

no code implementations5 Apr 2022 Rishab Sharma, Fuxiang Chen, Fatemeh Fard

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.

Code Comment Generation Comment Generation +4

An Exploratory Study on Code Attention in BERT

no code implementations5 Apr 2022 Rishab Sharma, Fuxiang Chen, Fatemeh Fard, David Lo

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.

Clone Detection Code Summarization

Salient Image Matting

no code implementations23 Mar 2021 Rahul Deora, Rishab Sharma, Dinesh Samuel Sathia Raj

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.

Image Matting Object +3

API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations

no code implementations19 Mar 2021 Ramin Shahbazi, Rishab Sharma, Fatemeh H. Fard

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.

Comment Generation Machine Translation

AlphaNet: An Attention Guided Deep Network for Automatic Image Matting

no code implementations7 Mar 2020 Rishab Sharma, Rahul Deora, Anirudha Vishvakarma

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.

Segmentation Semantic Image Matting +1

Retrieving Similar E-Commerce Images Using Deep Learning

3 code implementations11 Jan 2019 Rishab Sharma, Anirudha Vishvakarma

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.

Fine-Grained Visual Recognition Image Retrieval +3

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