Source-linked AI summary
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, Martin Wattenberg
TL;DR
Understanding embedding geometry requires tools that support interactive exploration beyond static dimensionality-reduction views. The paper presents the Embedding Projector, a TensorFlow-integrated system tailored to neighborhoods, global structure, and meaningful directions, while identifying embedding comparison and automated direction discovery as future work.
Problem
Researchers and developers need to explore embedding geometry, but high dimensionality, non-interactive existing tools, and semantically opaque basis vectors complicate that analysis.
Method
The paper presents the Embedding Projector, a web application combining interactive visualization with embedding-specific analysis features and TensorFlow integration.
Results
The Projector supports interpreting machine-learning models through local-neighborhood exploration, global-geometry analysis, and investigation of semantically meaningful vectors.
Takeaways & Limitations
The tool provides a shared interface for exploring embedding structure and interpreting TensorFlow models across these three analysis tasks.
Takeaways & Limitations
The current visualization does not yet support embedding comparison or automated generation and testing of hypotheses about meaningful directions.
Abstract
from arXiv · showhide
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of embeddings.
1 Introduction
Embeddings map inputs into Euclidean space, but understanding their local neighborhoods and global geometry is difficult when the coordinates are high-dimensional and semantically opaque. The Embedding Projector addresses this need with interactive visualization focused on machine-learning embeddings and meaningful directions.
- Embeddings map input data to points in Euclidean space for analysis of machine-learning model behavior.Users may inspect local neighbors or global geometric relationships among embedded points.
- Dimensionality reduction projects hundreds-dimensional embeddings into more approachable two- or three-dimensional views.Existing tools commonly provide such projections but are largely non-interactive.
- Static views are inadequate for exploring high-dimensional data because users need rapid switching, zooming, filtering, and linked inspection.Prior visualization toolkits indicate that rich interactivity and multiple linked views are helpful.
- Embedding coordinates differ from conventional high-dimensional data because their basis vectors typically lack intrinsic meaning.Discovering semantically significant directions can itself be an analytical goal.
- The Embedding Projector provides interactive visualization and analysis of high-dimensional data, optimized for machine-learning use cases and meaningful directions.It is presented as a general-purpose system with features tailored to embedding analysis.
2 How Users (Want to) Interpret Embeddings
Interviews and existing-tool usage revealed three recurring ways users want to interpret embeddings: inspecting local neighborhoods, examining global geometry, and finding meaningful directions.
- Interviews with engineers and researchers, supplemented by existing-tool usage, identified three consistent themes in embedding interpretation.These themes organize the users’ desired analysis workflows.
- Task 1. Exploring local neighborhoods: Users inspect nearest neighbors to verify that nearby points are semantically related and establish trust in an algorithm.This is the local-neighborhood task.
- Task 2. Viewing global geometry and finding clusters: Users seek large clusters of related points and the global geometry of embedding spaces.This task concerns structure beyond an individual point’s neighborhood.
- Task 3. Finding meaningful “directions”: Researchers investigate semantically significant directions, such as the vector from “man” to “woman,” but existing tools did not support this use case.Users considered discovering such directions interesting and worthwhile.
3 The Embedding Projector Application
The Embedding Projector is a web application integrated with TensorFlow that combines dimensionality-reduction views, interactive exploration, nearest-neighbor inspection, point selection, and collaboration features. Its custom projections support investigating meaningful directions in embedding space.
- The Embedding Projector accepts arbitrary high-dimensional data or TensorFlow model tensors through a standalone or integrated web application.Users can upload text-formatted data or use TensorFlow checkpoints.
- The application offers three dimensionality-reduction methods, each producing either a two- or three-dimensional view.The methods are two linear approaches and one nonlinear approach.
- Dimensionality reduction: PCA computes the top 10 principal components and supports projections onto any combination of two or three components.The paper notes that PCA is often effective for examining global geometry.
- Dimensionality reduction: t-SNE provides two- and three-dimensional client-side views and often preserves local structure relevant to neighborhood and cluster exploration.The method is described as supporting both local-neighborhood and global-geometry tasks.
- Custom projections: Custom projections define an axis from the difference between centroids of two search- or regular-expression-matched point sets.In Smart Reply data, this view revealed a regular relationship between phrases ending in periods and exclamation points.
- Interacting with the visualizations: Selecting a point displays its nearest neighbors and distances, while selected subsets can be isolated for further PCA or t-SNE analysis.Selections can be created from nearest neighbors or search results.
- Collaborative Features: Users can save visualization states, including computed t-SNE coordinates, and let others walk through bookmarked views.This supports sharing specific analyses and collaborative inspection.
4 Conclusion
The Embedding Projector is presented as a TensorFlow-integrated tool tailored to interpreting embeddings through local neighborhoods, global geometry, and meaningful vectors. The authors identify embedding comparison and automated discovery of meaningful directions as future work.
- The Embedding Projector targets three machine-learning interpretation tasks: local neighborhoods, global geometry, and semantically meaningful vectors.The tool is customized for these tasks rather than conventional high-dimensional visualization alone.
- The Projector is part of TensorFlow and supports analysis and interpretation of TensorFlow models.The conclusion describes this integration as seamless.
- Future work includes visually comparing embeddings from multiple model versions or across model changes over time.The authors state that this comparison would require nontrivial additions to current visualizations.
- Future research could make meaningful directions easier to discover by having the computer generate and test hypotheses automatically.The current interface makes it easy to explore hypotheses but does not automate this process.