changliu00/causal-semantic-generative-model
Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)
This tool helps machine learning engineers and researchers build more robust predictive models. It processes various image datasets, like medical scans or product photos, to create models that can accurately predict outcomes even when faced with new, unfamiliar data not seen during training. The output is a model that generalizes better across different environments or conditions, reducing performance drops.
No commits in the last 6 months.
Use this if you need to develop machine learning models that maintain high accuracy when deployed in new, unseen environments or with data that differs from your training set.
Not ideal if you are looking for a general-purpose machine learning library for standard supervised learning tasks where out-of-distribution generalization is not a primary concern.
Stars
77
Forks
3
Language
Python
License
MIT
Category
Last pushed
Apr 18, 2022
Commits (30d)
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