Avalanche
GitHubAPI DocCL-BaselinesAvalanche-RLOnline CL Repo
Avalanche - v0.3.1
Avalanche - v0.3.1
  • Avalanche: an End-to-End Library for Continual Learning
  • 📌Getting Started
    • Introduction
    • Current Release
    • How to Install
    • Learn Avalanche in 5 Minutes
  • 📙From Zero to Hero Tutorial
    • Introduction
    • Models
    • Benchmarks
    • Training
    • Evaluation
    • Loggers
    • Putting All Together
    • Extending Avalanche
    • Contribute to Avalanche
  • How-Tos
    • AvalancheDataset
      • avalanche-datasets
      • avalanche-transformations
    • Dataloaders, Buffers, and Replay
    • Save and load checkpoints
  • 📝Examples
    • Models
    • Benchmarks
    • Training
    • Evaluation
    • Loggers
  • 💻Code Documentation
    • Avalanche API
  • How to Contribute
    • Guidelines
  • ❓Questions and Issues
    • Ask Your Question
    • Add Your Issue
    • Request a Feature
    • Give Feedback
    • FAQ
  • 👪About Us
    • The People
    • Join Us!
    • Slack
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  1. Questions and Issues

FAQ

Frequently Asked Questions

PreviousGive FeedbackNextThe People

Last updated 2 years ago

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In this page we answer frequently asked questions about the library. We know these to be mostly pain points we need to address as soon as possible in the form of better features o better documentation.

How can I create a stream of experiences based on my own data?

You can use the : such utils in Avalanche allows you to build a stream of experiences based on an AvalancheDataset (or PyTorchDataset), or directly from PyTorch tensors, paths or filelists.

Why some Avalanche strategies do not work on my dataset?

We cannot guarantee each strategy implemented in Avalanche will work in any possible setting. A continual learning algorithm implementation is accepted in Avalanche if it can reproduce at least a portion of the original paper results. In the project we make sure reproducibility is maintained for those with every main avalanche release.

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Benchmark Generators
CL-Baseline