jindongwang/transferlearning

★ 14,334PythonAudience · researcherComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((Transfer Learning))
    Topics
      Domain adaptation
      Few-shot learning
      Multi-task learning
    Resources
      Research papers
      Video tutorials
      Datasets
    Code
      Python examples
      Domain adaptation
    Audience
      ML researchers
      Practitioners

mindmap root((Transfer Learning)) Topics Domain adaptation Few-shot learning Multi-task learning Resources Research papers Video tutorials Datasets Code Python examples Domain adaptation Audience ML researchers Practitioners

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Things people build with this

USE CASE 1

Find the latest domain adaptation research papers organized by topic and year without searching the open web.

USE CASE 2

Run example code for domain adaptation methods without building experiments from scratch.

USE CASE 3

Use the curated dataset list to pick a benchmark for comparing transfer learning approaches.

USE CASE 4

Follow English or Chinese tutorials to learn transfer learning concepts from the ground up.

Tech stack

Python

Getting it running

Difficulty · moderate Time to first run · 1h+

Running code examples requires a Python ML environment and may need specific benchmark datasets downloaded separately.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

This repository is a curated collection of resources about transfer learning, a technique in machine learning where knowledge gained from training an AI model on one problem is applied to help solve a different but related problem. Rather than a software tool you install and run, it is more like a structured library of papers, tutorials, code examples, datasets, and references gathered in one place. Transfer learning is useful because training a powerful AI model from scratch typically requires enormous amounts of data and computing time. If a model has already learned to recognize patterns in one domain, those learned patterns can often give a head start when tackling a new domain. Related concepts covered here include domain adaptation (adjusting a model trained on one type of data to work on a different type), domain generalization (building models that work across many different contexts without needing to be retrained), few-shot learning (training models that can learn from very few examples), and multi-task learning (training a single model to handle several different tasks at once). The repository includes links to academic research papers organized both by topic and by publication date, video tutorials in both English and Chinese, slide decks, and a book written by the repository's author. There is also runnable code in the code directory, covering various domain adaptation methods. Datasets and benchmark results are catalogued to help researchers compare approaches. The README notes that the repository has been cited in papers published at several top academic conferences and journals in the machine learning field. The material is aimed at researchers and practitioners already working in machine learning who want a single organized starting point for the transfer learning literature. The code is written in Python. The repository is maintained by Jindong Wang and is released under the MIT license.

Copy-paste prompts

Prompt 1

Using the domain adaptation code in the jindongwang/transferlearning repo, show me how to adapt a model trained on one image dataset to work on a different one.

Prompt 2

Find papers on few-shot learning from the jindongwang/transferlearning repository and summarize the most-cited ones published after 2020.

Prompt 3

Show me how to run one of the domain adaptation methods in the transferlearning/code directory on a custom image classification dataset.

Prompt 4

Which datasets listed in the jindongwang/transferlearning repository are suitable for text-based domain adaptation benchmarks?

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