Search for: All records

Traditional training algorithms for Gumbel Softmax Variational Autoencoders (GS-VAEs) typically rely on an annealing scheme that gradually reduces the Softmax temperature τ according to a given function. This approach can lead to suboptimal results. To improve the performance, we propose a parallel framework for GS-VAEs, which embraces dual latent layers and multiple sub-models with diverse temperature strategies. Instead of relying on a fixed function for adjusting τ, our training algorithm uses loss difference as performance feedback to dynamically update each sub-model’s temperature τ, which is inspired by the need to balance exploration and exploitation in learning. By combining diversity in temperature strategies with the performance-based tuning method, our design helps prevent sub-models from becoming trapped in local optima and finds the GS-VAE model that best fits the given dataset. In experiments using four classic image datasets, our model significantly surpasses a standard GS-VAE that employs a temperature annealing scheme across multiple tasks, including data reconstruction, generalization capabilities, anomaly detection, and adversarial robustness. Our implementation is publicly available at https://github.com/wxzg7045/Gumbel-Softmax-VAE-2024/tree/main. 

This paper extends the star set reachability approach to verify the robustness of feed-forward neural networks (FNNs) with sigmoidal activation functions such as Sigmoid and TanH. The main drawbacks of the star set approach in Sigmoid/TanH FNN verification are scalability, feasibility, and optimality issues in some cases due to the linear programming solver usage. We overcome this challenge by proposing a relaxed star (RStar) with symbolic intervals, which allows the usage of the back-substitution technique in DeepPoly to find bounds when overapproximating activation functions while maintaining the valuable features of a star set. RStar can overapproximate a sigmoidal activation function using four linear constraints (RStar4) or two linear constraints (RStar2), or only the output bounds (RStar0). We implement our RStar reachability algorithms in NNV and compare them to DeepPoly via robustness verification of image classification DNNs benchmarks. The experimental results show that the original star approach (i.e., no relaxation) is the least conservative of all methods yet the slowest. RStar4 is computationally much faster than the original star method and is the second least conservative approach. It certifies up to 40% more images against adversarial attacks than DeepPoly and on average 51 times faster than the star set. Last but not least, RStar0 is the most conservative method, which could only verify two cases for the CIFAR10 small Sigmoid network,δ= 0.014. However, it is the fastest method that can verify neural networks up to 3528 times faster than the star set and up to 46 times faster than DeepPoly in our evaluation. 

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. For this reason, Binary Neural Networks (BNNs) are widely used on edge devices, such as microcomputers. However, architecture specifics and floating-point number usage result in an increased computational operations complexity. Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying BNNs.This paper proposes an approach to verify BNNs on continuous input space using star reachability analysis. Our approach can compute both exact and overapproximate reachable sets of BNNs with Sign activation functions and use them for verification. The proposed approach is also efficient in constructing a complete set of counterexamples in case a network is unsafe. We implemented our approach in NNV, a neural network verification tool for DNNs and learning-enabled Cyber-Physical Systems. The experimental results show that our star-based approach is less conservative, more efficient, and scalable than the recent SMT-based method implemented in Marabou. We also provide a comparison with a quantization-based tool EEVBNN.