weitianxin/awesome-distribution-shift
A curated list of papers and resources about the distribution shift in machine learning.
This is a curated list of research papers and resources focused on machine learning models that struggle when data shifts unexpectedly. It helps researchers and practitioners find materials related to building models that perform reliably even when deployed in environments different from their training data. You'll find academic papers, benchmarks, and code for improving model robustness across various data types like images, text, and time series.
125 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or practitioner investigating how to make your models robust to changes in data distribution, or if you need to understand existing solutions and benchmarks in this area.
Not ideal if you are looking for an off-the-shelf software tool or a step-by-step tutorial for implementing distribution shift solutions.
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Aug 05, 2023
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