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[code

Pytorch implementation of our paper accepted by ECCV2022 -- Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks

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Overview

Pytorch implementation of our paper accepted by ECCV2022 -- Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks

Topics & capabilities

quantizationsuper-resolutionaccelerationcompression

Repository information

Repository
zysxmu/DDTB ↗
Repository owner
zysxmu
Primary language
Python
Repository code license
Not recorded
Maintenance status
Not archived at the recorded check
Forked repository
No
GitHub stars
30
Forks
4
Default branch
main
Last repository push
Sep 13, 2022
Latest release
1.0 ↗
Release published
Sep 13, 2022

Source: GitHub API · observed Oct 11, 2026. Stars and forks are recorded counts. Repository code licenses may differ from dataset or model-weight licenses.

Source links & provenance

https://github.com/zysxmu/ddtb ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

Explore related resources

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Website

GitHub

github.com

Developer platform for hosting and collaborating on software repositories. This record links to the platform homepage.

Page language
en
Directory citations
3
Discovery sources
3
ai-quant-directorypysystemtradesetup
98 Authority 98 / 100 Cited by 3 resources in this directory 3 discovery sources Directory record updated: 2026-10-14 How scoring works →
Screenshot unavailableResource information and source links remain available. QuantConnect/mcp-server — reviewed source website screenshot
RepositoryChecked capture

QuantConnect/mcp-server

github.com

Official QuantConnect MCP server: write Python trading strategies, run cloud backtests, and deploy live algorithms

GitHub stars
78
Language
Python
Code license
Apache-2.0
ai-in-financemcp-serversgithub
97 Authority 97 / 100 Cited by 3 resources in this directory 3 discovery sources Directory record updated: 2026-10-11 How scoring works →

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