Transparent, Trustworthy & Explainable AI for Computer Vision
How It Works
Leap2X applies advanced mathematical techniques across nodes in the neural network’s computational graph to uncover critical patterns and evaluate the contribution of individual features to model predictions.
Our proprietary algorithms design an intuitive latent space that deciphers your model’s decision pathways, highlights meaningful data clusters, and provides actionable insights into its underlying behavior and interpretability.
Reliability
Guided Error Analysis
Clustering:
Automatically group failing samples with common root causes, providing key metrics to evaluate your model’s quality and generalization.
Sample analysis:
Explore your model’s interpretation of individual samples and identify the key factors influencing predictions.
Visualization engine:
View your model’s performance and breakdowns across various sample characteristics, with the ability to drill down into specific populations for deeper insights.
Objectivity
Dataset Architecture
Design unbiased, balanced datasets that reflect practical and effective data distribution for optimal model training.
Scoring
Measure your datasets for quality in terms of variance, density, entropy, and balance to ensure they support accurate model predictions.
Data Clean-up
Identify and remove repetitive, redundant, ambiguous, or mislabeled data, improving the integrity of your datasets.
Labeling Prioritization
Identify the most critical samples for labeling to improve dataset variance and ensure accurate representation of data that may be causing model failures.
Validity
Deep Unit Testing
Confirm that issues are fully resolved without introducing regressions, while scanning for potential unknown problems.
Split Test Sets
Create multiple unit tests by clustering model features or conducting cross-searches on sample characteristics, and validate them simultaneously to ensure model reliability.
Guided Selection
Drill down into specific sample groups to define, track, and visualize tailored tests using an intuitive dashboard.
Automatic Scan
Run unsupervised analysis to detect suspicious clusters and anomalies in your model, without requiring a deep understanding of its internal workings.
Efficiency
Keep Your Team in the Loop
Share insights, findings, and results effortlessly with your team to encourage collaboration and transparency.
Pull Requests
Enable developers to quickly review changes and their associated tests, understanding the impact on the model’s interpretation.
Issues
Automatically track and document the issue-resolution lifecycle, including tests, references, and related samples, for complete transparency.
Reporting
Easily generate reports to monitor model evolution and keep everyone informed, ensuring a culture of collaboration.
Flexibility and Integration
Leap2X is a fully Kubernetes-based solution that operates seamlessly on any cloud or on-premise infrastructure. It integrates quickly with your existing model frameworks and datasets, supporting both structured and unstructured data types (images, text, time-series, tabular data, etc.).
