Frequently Asked Questions
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 Benchmark Generators: 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 CL-Baseline project we make sure reproducibility is maintained for those with every main avalanche release.
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