Xpdeep offers a self-explainable deep learning framework that generates deep models with integrated, intelligible explanations, enabling users to understand model decisions and inferences without additional computational costs. This technology addresses the opacity of traditional deep learning models, enhancing trust, compliance, and risk management for businesses by providing clear insights into model behavior and performance.
Funding
$650K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional deep learning models operate as "black boxes," making it difficult to understand the reasoning behind their decisions. This opacity hinders trust, limits adoption, and creates challenges for compliance and risk management, especially in regulated industries.
Solution
Xpdeep offers a self-explainable deep learning framework that generates deep models with integrated, intelligible explanations, eliminating the need for post-hoc analysis. By design, the framework explains both the model's global behavior and individual inferences at local scales without compromising performance or incurring additional computational costs. This allows users to understand model decisions, identify biases, detect weaknesses, and ensure robustness, fostering trust and facilitating compliance. The framework includes Python libraries for training explainable models and a visualization app for exploring model internals.
Target Audience
Xpdeep targets data scientists, AI developers, and business stakeholders in industries such as finance, healthcare, and manufacturing who require transparent, trustworthy, and compliant deep learning models.
Features
- Self-explainable deep learning: Models are explainable by design, not as an afterthought.
- Global and local explanations: Understand both the overall model behavior and individual inference reasoning.
- No performance overhead: Explainability is integrated without sacrificing accuracy or speed.
- Python API: Seamless integration with PyTorch 2.4 for training and analysis.
- Visualization app: Interactive interface for exploring model decisions and explanations.
- Support for various data types: Compatible with tabular, text, images, and time series data.
- Explain pre-trained models: Apply Xpdeep to existing models to gain insights.
- Counterfactual and fairness analyses: Identify biases and understand the impact of different factors.