Context Awesome

awesome-multi-objective-deep-learning

github.com/baijiong-lin/awesome-multi-objective-deep-learning ↗

A comprehensive list of gradient-based multi-objective optimization algorithms in deep learning.

120

GitHub Stars

66

Curated Resources

5

Categories

21 hours ago

Last Refreshed

Related SurveyFinding a Single SolutionFinding a Finite Set of SolutionsFinding an Infinite Set of SolutionsResources

Use this list with your AI agent

Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:

"Show me software resources from awesome-multi-objective-deep-learning"

Installation instructions →

What's inside

Related Survey

Resources

PaperSoftware
PaperSoftware

Finding a Single Solution

PaperLoss Balancing Methods
PaperLoss Balancing Methods
PaperGradient Balancing Methods
PaperLoss Balancing Methods
PaperLoss Balancing Methods
PaperGradient Balancing Methods

Finding a Finite Set of Solutions

PaperMethods without Using Preference Vectors
PaperMethods Based on Preference Vectors
PaperMethods Based on Preference Vectors
PaperMethods Based on Preference Vectors
PaperMethods Based on Preference Vectors
PaperMethods Based on Preference Vectors

Finding an Infinite Set of Solutions

PaperModel Combination-based Methods
PaperHypernetwork-based Methods
PaperHypernetwork-based Methods
PaperHypernetwork-based Methods
PaperHypernetwork-based Methods
PaperHypernetwork-based Methods

Showing a sample of 66 resources. View the full list on GitHub →