一站式机器学习验证框架，显著提升合规性与效率。
DeepBridge: A Unified and Production-Ready Framework for Multi-Dimensional Machine Learning Validation
- 统一5大验证模块，支持公平性、鲁棒性等多维度评估
- 验证时间减少89%，10项特征公平性检测全覆盖
- 适合需合规审计的金融、医疗等生产级场景
我们提出DeepBridge，一个8万行代码的Python库，统一实现多维验证、自动合规检查、知识蒸馏与合成数据生成。其包含：(i) 5个验证套件（公平性含15项指标，鲁棒性弱项检测，不确定性通过分位数预测，韧性涵盖5类数据漂移，超参数敏感性）；(ii) 自动化EEOC/ECOA/GDPR合规验证；(iii) 多格式报告系统（交互式/静态HTML、PDF、JSON）；(iv) 基于元学习的HPM-KD知识蒸馏框架；(v) 借助Dask实现可扩展合成数据生成。6个案例研究（信贷、招聘、医疗、房贷、保险、欺诈）表明：验证时间减少89%（17分钟对150分钟），公平性违规检测覆盖率达100%（10/10特征，现有工具仅2/10），报告生成仅需几分钟。HPM-KD在压缩比2.3–7倍时表现更优（CIFAR100上准确率提升1.00–2.04个百分点，p<0.05），证实知识蒸馏在大教师-学生差距下依然有效。20人可用性测试显示：SUS评分87.5（前10%，“优秀”），成功率达95%，认知负荷低（NASA-TLX 28/100）。DeepBridge开源于MIT协议，项目地址：https://github.com/deepbridge/deepbridge，文档详见：https://deepbridge.readthedocs.io
原文摘要 · Abstract (English)
We present DeepBridge, an 80K-line Python library that unifies multi-dimensional validation, automatic compliance verification, knowledge distillation, and synthetic data generation. DeepBridge offers: (i) 5 validation suites (fairness with 15 metrics, robustness with weakness detection, uncertainty via conformal prediction, resilience with 5 drift types, hyperparameter sensitivity), (ii) automatic EEOC/ECOA/GDPR verification, (iii) multi-format reporting system (interactive/static HTML, PDF, JSON), (iv) HPM-KD framework for knowledge distillation with meta-learning, and (v) scalable synthetic data generation via Dask. Through 6 case studies (credit scoring, hiring, healthcare, mortgage, insurance, fraud) we demonstrate that DeepBridge: reduces validation time by 89% (17 min vs. 150 min with fragmented tools), automatically detects fairness violations with complete coverage (10/10 features vs. 2/10 from existing tools), generates audit-ready reports in minutes. HPM-KD demonstrates consistent superiority across compression ratios 2.3--7x (CIFAR100): +1.00--2.04pp vs. Direct Training (p<0.05), confirming that Knowledge Distillation is effective at larger teacher-student gaps. Usability study with 20 participants shows SUS score 87.5 (top 10%, ``excellent''), 95% success rate, and low cognitive load (NASA-TLX 28/100). DeepBridge is open-source under MIT license at https://github.com/deepbridge/deepbridge, with complete documentation at https://deepbridge.readthedocs.io
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。