nianlonggu/MasterThesis_Autoencoder-based-Super-Resolution

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Created over 6 years ago
Updated about 6 years ago

nianlonggu/MasterThesis_Autoencoder-based-Super-Resolution

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Master thesis project. In this project, an autoencoder model is designed to achieve single image super resolution.

MemSum

Code for ACL 2022 paper on the topic of long document summarization: MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes

Jupyter Notebook

Local-Citation-Recommendation

Code for ECIR 2022 paper Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking

WhisperSeg

Code for ICASSP 2024 paper WhisperSeg: Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection

MemSum-DQA

Adapting an Efficient Long Document Extractive Summarizer for Document Question Answering

Jupyter Notebook

SciLit

ACL23 Demo SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation

Floating-Random-Walk-Method-for-Capacitance-Extraction

Using the floating random walk method to extract the inter-connect capacitance in the VLSI

reverse-variational-autoencoder

WACV 2020 paper: Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection Pytorch Implementation

Jupyter Notebook

Super-Resolution-Spectrum-Analysis

Analyze the image quality in the frequency domain and compare the performance of different SISR models

Autoencoder-based-Super-Resolution

Master thesis project. In this project, an autoencoder model is designed to achieve single image super resolution.

SupportVectorMachine

simple implementation of the support vector machine algorithm, for detail see my blog https://nianlonggu.com

Jupyter Notebook