Research notes: Self Supervised
Overview
A structured set of research notes on Self Supervised, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results.
Expect rough edges — this is a working sketch, not a polished release.
Scope and limitations
Exploratory by design: no benchmark claims, no completed ablations, no released code, no trained checkpoint. The references are a starting point for verification, not evidence that the study has run.
What is covered
- the scope of the research question and likely confounders
- a proposed comparison with matched baselines
- concrete evaluation context such as task-appropriate public benchmarks named in the main note
- reproducibility checks, failure modes, and open questions
- topic-relevant references
Open questions
- which baselines are actually comparable under matched capacity?
- what is the minimum data exposure for a meaningful signal?
- which confounders would invalidate a naive comparison?
Acknowledgments
This repo borrows ideas freely from public reference implementations; credit to their authors.
Citation
If this repository is useful for your work, a link back is appreciated. No formal citation is required yet.
How to read this repository
Read summary.md first. Anything labeled as a plan or hypothesis is not a result. Future results should ship with dataset versions, commands, seeds, hardware and raw logs.
Files
summary.md— primary artifactREADME.md— this documentationconfig.json— architecture configurationtraining_args.json— default experiment settingsmodel.safetensors— initialization checkpoint
License
Distributed under the cc0-1.0 license. External datasets keep their own terms — check them separately.
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