DataMetaMap aims to compare datasets within a unified vector space to identify semantic similarities. The core idea is that if a model performs well on one dataset, it will likely perform well on semantically similar datasets nearby in embedding space.
-
Literature Review
Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices. -
Data Collection
Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats. -
Planning and Specifications
Define technical specifications and success criteria based on research findings and data availability.
-
Core Algorithm Development
Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them. -
Testing and Quality Assurance
Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods. -
Benchmarking and Visualization
Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results.
-
Technical Report
Document the methodology, experimental setup, and findings in a comprehensive technical report. -
User and Developer Documentation
Create detailed documentation for users and contributors, including setup guides and API references. -
Demo Examples and Blog Post
Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.
-
Literature Review
Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices. -
Baseline Selection
Identify and select baseline methods from literature for comparison during benchmarking.Description (done by Meshkov Vladislav):
- Establish baselines for each embedding method as specified in the paper
- Assess baselines from the literature and determine their appropriateness for our benchmarking framework
- Conduct a literature review to identify similar papers and gather additional straightforward baselines for meaningful comparison
- Document baseline descriptions in the benchmark specifications, along with rationale for their inclusion
-
Data Collection
Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats. -
Data Preprocessing Pipeline
Design and implement preprocessing steps to handle different dataset formats and ensure consistent input for embedding methods.Description (done by Minashkin Vladislav):
- Handle diverse data types: images, text, tabular, and time series with type-specific loaders
- Fill missing values and remove bad data points
- Save all settings for exact reproduction
-
Evaluation Metrics Definition
Define quantitative metrics to evaluate embedding quality and similarity measurement accuracy.Description (done by Stepanov Ilya):
- Define cosine similarity, Euclidean distance and kernel-based distance as core metrics to evaluate geometric separability and structural relationships between dataset embeddings in the latent space
- Define Maximum Mean Discrepancy (MMD) metric as described in the original paper
- Ensure that all embedding methods and baselines will be evaluate using all metrics so that comparison across methods is consistent and reproducible
-
Planning and Specifications
Define technical specifications and success criteria based on research findings and data availability.
-
Core Algorithm Development
Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them. -
Baseline Implementations
Implement selected baseline methods from literature for comparison. -
Testing and Quality Assurance
Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods. -
Performance Optimization
Profile and optimize code for memory efficiency and computational speed, especially for large datasets. -
Error Handling and Logging
Implement robust error handling and logging mechanisms for debugging and monitoring. -
Benchmarking and Visualization
Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results.
-
Technical Report
Document the methodology, experimental setup, and findings in a comprehensive technical report. -
User and Developer Documentation Build documentation.
Description (done by Papay Ivan):
- create detailed documentation for users and contributors, including setup guides and API references
- create github.io page where user can find documentation for all classes and their methods
- Github.io page must have headers for functions and links to their each source code.
-
Demo Examples and Blog Post
Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights. -
Benchmark Results Repository
Publish benchmark results, precomputed embeddings, and similarity matrices in a public repository for reproducibility. -
Future Work Roadmap
Outline potential extensions, improvements, and research directions based on current findings.