NeVer 2.0: Learning, Verification and Repair of Deep Neural Networks

TL;DR

NeVer 2.0 is an integrated system designed for automated learning, verification, and repair of deep neural networks, aiming to improve scalability and fault correction capabilities.

Contribution

It extends the original NeVer system to deep networks by combining modern learning frameworks with verification algorithms.

Findings

Prototype implementation of NeVer 2.0

Successful integration of learning and verification modules

Enhanced scalability for deep neural network analysis

Abstract

In this work, we present an early prototype of NeVer 2.0, a new system for automated synthesis and analysis of deep neural networks.NeVer 2.0borrows its design philosophy from NeVer, the first package that integrated learning, automated verification and repair of (shallow) neural networks in a single tool. The goal of NeVer 2.0 is to provide a similar integration for deep networks by leveraging a selection of state-of-the-art learning frameworks and integrating them with verification algorithms to ease the scalability challenge and make repair of faulty networks possible.