We propose a neural network-based computational framework for the simultaneous optimization of structural topology, curved layers, and path orientations to achieve strong anisotropic strength in fiber-reinforced thermoplastic composites while ensuring manufacturability. Our framework employs three implicit neural fields to represent geometric shape, layer sequence, and fiber orientation. This enables the direct formulation of both design and manufacturability objectives - such as anisotropic strength, structural volume, machine motion control, layer curvature, and layer thickness - into an integrated and differentiable optimization process. By incorporating these objectives as loss functions, the framework ensures that the resultant composites exhibit optimized mechanical strength while remaining its manufacturability for filament-based multi-axis 3D printing across diverse hardware platforms. Physical experiments demonstrate that the composites generated by our co-optimization method can achieve an improvement of up to 33.1% in failure loads compared to composites with sequentially optimized structures and manufacturing sequences.
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A procedural program is the representation of a family of assets that share the same structural or semantic properties, whose final appearance is determined by different parameter assignments. Identifying the parameter values that define a desired asset is usually a time-consuming operation, since it requires manually tuning parameters separately and in a non-intuitive manner. In the domain of procedural patterns, recent works focused on estimating parameter values to match a target render or sketch, using parameter optimization or inference via neural networks. However, these approaches are neither fast enough for interactive design nor precise enough to give direct control. In this work, we propose an interactive method for procedural parameter estimation based on the idea of scaffolded procedural patterns. A scaffolded procedural pattern is a sequence of procedural programs that model a pattern in a coarse-to-fine manner, in which the desired pattern appearance is reached step-by-step by inheriting previously optimized parameters. Through scaffolding, patterns are more straightforward to sketch for users and easier to optimize for most algorithms. In our implementation, patterns are represented as procedural signed distance functions whose parameters are estimated with a gradient-free optimization method that runs in real-time on the GPU. We show that scaffolded patterns can be created with a node-based interface familiar to artists. We validate our approach by creating and interactively editing several scaffolded patterns. We show the effectiveness of scaffolding through a user study, where scaffolding enhances both the output quality and the editing experience with respect to approaches that optimize the procedural parameters all at once. We also perform a comparison with previous strategies and provide several recordings of real-time editing sessions in the accompanying materials.
Heat exchangers are critical components in a wide range of engineering applications, from energy systems to chemical processing, where efficient thermal management is essential. The design objectives for heat exchangers include maximizing the heat exchange rate while minimizing the pressure drop, requiring both a large interface area and a smooth internal structure. State-of-the-art designs, such as triply periodic minimal surfaces (TPMS), have proven effective in optimizing heat exchange efficiency. However, TPMS designs are constrained by predefined mathematical equations, limiting their adaptability to freeform boundary shapes. Additionally, TPMS structures do not inherently control flow directions, which can lead to flow stagnation and undesirable pressure drops. This paper presents DualMS, a novel computational framework for optimizing dual-channel minimal surfaces specifically for heat exchanger designs in freeform shapes. To the best of our knowledge, this is the first attempt to directly optimize minimal surfaces for two-fluid heat exchangers, rather than relying on TPMS. Our approach formulates the heat exchange maximization problem as a constrained connected maximum cut problem on a graph, with flow constraints guiding the optimization process. To address undesirable pressure drops, we model the minimal surface as a classification boundary separating the two fluids, incorporating an additional regularization term for area minimization. We employ a neural network that maps spatial points to binary flow types, enabling it to classify flow skeletons and automatically determine the surface boundary. DualMS demonstrates greater flexibility in surface topology compared to TPMS and achieves superior thermal performance, with lower pressure drops while maintaining a similar heat exchange rate under the same material cost. The project is open-sourced at https://github.com/weizheng-zhang/DualMS.
This paper presents a scalable framework for efficiently discovering the performance gamut of different processes. Gamut boundaries comprise the set of highest-performing solutions within a design space. While sampling methods are often inefficient or prone to premature convergence, Bayesian optimization struggles with taking advantage of existing large-scale parallel computation or experimentation. To address these challenges, we utilize Bayesian neural networks as scalable surrogates for performance prediction and uncertainty estimation. We further introduce a novel acquisition function that combines the diversity-driven exploration of stochastic optimization with the information-efficient exploitation of Bayesian optimization. This enables generating large, high-quality batches of samples. Our approach leverages large batch sizes to reduce the number of iterations needed for optimization. We demonstrate its effectiveness on real-world engineering and robotic problems, achieving faster and more extensive discovery of the performance gamut. Code and data are available at https://gitlab.mpi-klsb.mpg.de/nansari/lbn_mobo.
Obtaining 3D faces with microscopic structures from a single unconstrained image is challenging. The complexities of wrinkles and pores at a microscopic level, coupled with the blurriness of the input image, raise the difficulty. However, the distribution of wrinkles and pores tends to follow a specialized pattern, which can provide a strong prior for synthesizing them. Therefore, a key to microstructure synthesis is a parametric wrinkles and pore model with controllable semantic parameters. Additionally, ensuring differentiability is essential for enabling optimization through gradient descent methods. To this end, we propose a novel framework designed to reconstruct facial micro-wrinkles and pores from naturally captured images efficiently. At the core of our framework is a differentiable representation of wrinkles and pores via a graph neural network (GNN), which can simulate the complex interactions between adjacent wrinkles by multiple graph convolutions. Furthermore, to overcome the problem of inconsistency between the blurry input and clear wrinkles during optimization, we proposed a Direction Distribution Similarity that ensures that the wrinkle-directional features remain consistent. Consequently, our framework can synthesize facial micro-structures from a blurry skin image patch, which is cropped from a natural-captured facial image, in around an average of 2 seconds. Our framework can seamlessly integrate with existing macroscopic facial detail reconstruction methods to enhance their detailed appearance. We showcase this capability on several works, including DECA, HRN, and FaceScape.
Pretend Benign: A Stealthy Adversarial Attack by Exploiting Vulnerabilities in Cooperative Perception
PDF ↗Recently, learning-based multi-agent cooperative perception has garnered widespread attention. However, the inherent vulnerabilities of neural networks, combined with the risks posed by cooperative communication as a wide-open backdoor, render these systems highly susceptible to adversarial attacks.Existing attack methods lack stealth as they perturb transmitted information indiscriminately, producing numerous false positives that are readily detected by consensus-based defenses. This paper proposes Pretend Benign (PB), a novel stealthy adversarial attack method that exploits vulnerabilities in cooperative perception to enable the attacker to disguise as a benign cooperator. To achieve this, we first introduce the Attack Region Selection (ARS) module, which divides the perception area into sub-regions based on confidence levels to pinpoint optimal attack locations. Then, we propose Multi-target Adversarial Perturbation Generation (MAPG), which maintains consensus, gain the victim's trust, and thereby reverse the normal cooperative role of perception. To mitigate the latency in adversarial signal generation and communication, we further propose a real-time attack by predicting future information through historical feature flow. Extensive experiments on the OPV2V and V2XSet datasets demonstrate that PB effectively bypasses state-of-the-art defense methods, underscoring its stealth and efficacy.
Implicit Neural Representations (INR) have been successfully employed for Arbitrary-scale Super-Resolution (ASR). However, INR-based models need to query the multi-layer perceptron module numerous times and render a pixel in each query, resulting in insufficient representation capability and low computational efficiency. Recently, Gaussian Splatting (GS) has shown its advantages over INR in both visual quality and rendering speed in 3D tasks, which motivates us to explore whether GS can be employed for the ASR task. However, directly applying GS to ASR is exceptionally challenging because the original GS is an optimization-based method through overfitting each single scene, while in ASR we aim to learn a single model that can generalize to different images and scaling factors. We overcome these challenges by developing two novel techniques. Firstly, to generalize GS for ASR, we elaborately design an architecture to predict the corresponding image-conditioned Gaussians of the input low-resolution image in a feed-forward manner. Each Gaussian can fit the shape and direction of an area of complex textures, showing powerful representation capability. Secondly, we implement an efficient differentiable 2D GPU/CUDA-based scale-aware rasterization to render super-resolved images by sampling discrete RGB values from the predicted continuous Gaussians. Via end-to-end training, our optimized network, namely GSASR, can perform ASR for any image and unseen scaling factors. Extensive experiments validate the effectiveness of our proposed method. The code and models are available at https://github.com/ChrisDud0257/GSASR.
Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning quantized latent representations. In this paper, our key finding is that minimizing the latent entropy is, to some extent, equivalent to maximizing the conditional source entropy, an insight that is deeply rooted in information-theoretic equalities. Building on this insight, we propose a novel structural regularization method for the neural image compression task by incorporating the negative conditional source entropy into the training objective, such that both the optimization efficacy and the model's generalization ability can be promoted. The proposed information-theoretic regularizer is interpretable, plug-and-play, and imposes no inference overheads. Extensive experiments demonstrate its superiority in regularizing the models and further squeezing bits from the latent representation across various compression structures and unseen domains.
Single-view 3D reconstruction aims to recover the complete 3D geometry and appearance of objects from a single RGB image. Due to incomplete image information and ambiguity, this task remains challenging. Existing methods struggle with the trade-off between local detail and global topology, and with interference from early RGB-depth fusion in signed distance function optimization. To address these challenges, we propose Dual-S3D, a novel framework for single-view 3D reconstruction. Our method employs a hierarchical dual-path feature extraction strategy based on stages that utilize convolutional neural networks to anchor local geometric details. In contrast, subsequent stages leverage a Transformer integrated with selective state-space model to capture global topology, enhancing scene understanding and feature representation. Additionally, we design an auxiliary branch that progressively fuses precomputed depth features with pixel-level features to decouple visual and geometric cues effectively. Extensive experiments on the 3D-FRONT and Pix3D datasets demonstrate that our approach significantly outperforms existing methods--reducing chamfer distance by 51%, increasing F-score by 33.6%, and improving normal consistency by 10.3%--thus achieving state-of-the-art reconstruction quality.
Generalizing neural networks to unseen target domains is a significant challenge in real-world deployments. Test-time training (TTT) addresses this by using an auxiliary self-supervised task to reduce the domain gap caused by distribution shifts between the source and target. However, we find that when models are required to perform multiple tasks under domain shifts, conventional TTT methods suffer from unsynchronized task behavior, where the adaptation steps needed for optimal performance in one task may not align with the requirements of other tasks. To address this, we propose a novel TTT approach called Synchronizing Tasks for Test-time Training (S4T), which enables the concurrent handling of multiple tasks. The core idea behind S4T is that predicting task relations across domain shifts is key to synchronizing tasks during test time. To validate our approach, we apply S4T to conventional multi-task benchmarks, integrating it with traditional TTT protocols. Our empirical results show that S4T outperforms state-of-the-art TTT methods across various benchmarks.
Vector graphics are essential in design, providing artists with a versatile medium for creating resolution-independent and highly editable visual content. Recent advancements in vision-language and diffusion models have fueled interest in text-to-vector graphics generation. However, existing approaches often suffer from over-parameterized outputs or treat the layered structure -- a core feature of vector graphics -- as a secondary goal, diminishing their practical use. Recognizing the importance of layered SVG representations, we propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts. Inspired by Neural Radiance Fields (NeRFs), NeuralSVG encodes the entire scene into the weights of a small MLP network, optimized using Score Distillation Sampling (SDS). To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique that strengthens the standalone meaning of each shape. We additionally demonstrate that utilizing a neural representation provides an added benefit of inference-time control, enabling users to dynamically adapt the generated SVG based on user-provided inputs, all with a single learned representation. Through extensive qualitative and quantitative evaluations, we demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.
Convolution and transposed convolution are fundamental operators widely used in neural networks. However, transposed convolution (a.k.a. deconvolution) does not serve as a true inverse of convolution due to inherent differences in their mathematical formulations. To date, no reverse convolution operator has been established as a standard component in neural architectures. In this paper, we propose a novel depthwise reverse convolution operator as an initial attempt to effectively reverse depthwise convolution by formulating and solving a regularized least-squares optimization problem. We thoroughly investigate its kernel initialization, padding strategies, and other critical aspects to ensure its effective implementation. Building upon this operator, we further construct a reverse convolution block by combining it with layer normalization, 1x1 convolution, and GELU activation, forming a Transformer-like structure. The proposed operator and block can directly replace conventional convolution and transposed convolution layers in existing architectures, leading to the development of ConverseNet. Corresponding to typical image restoration models such as DnCNN, SRResNet and USRNet, we train three variants of ConverseNet for Gaussian denoising, super-resolution and deblurring, respectively. Extensive experiments demonstrate the effectiveness of the proposed reverse convolution operator as a basic building module. We hope this work could pave the way for developing new operators in deep model design and applications.
SMP-Attack: Boosting the Transferability of Feature Importance-based Adversarial Attack with Semantics-aware Multi-granularity Patchout
PDF ↗Transfer-based attacks pose a significant security threat to deep neural networks (DNNs), due to their strong performance on unseen models in real-world black-box scenarios. Building on this, feature importance-based attacks further improve the transferability of adversarial examples by effectively suppressing model-specific feature patterns. However, existing methods primarily focus on single-granularity patch and single-stage training, leading to suboptimal solutions. To address these limitations, we propose a general multi-stage optimization framework based on Semantics-aware Multi-granularity Patchout, dubbed as SMP-Attack. Compared to the non-deformable/regular patch definition, we incorporate multi-granularity into the generation process of deformable/irregular patches, thereby enhancing the quality of the computed aggregate gradient. In contrast to conventional joint optimization of multi-layer losses, we introduce an effective multi-stage training strategy that systematically explores significant model-agnostic features from shallow to intermediate layers. Employing the ImageNet dataset, we conduct extensive experiments on undefended/defended CNNs and ViTs, which unequivocally demonstrate the superior performance of our proposed SMP-Attack over current state-of-the-art methods in black-box scenarios. Furthermore, we assess the compatibility of our multi-stage optimization, which supersedes single-stage training employed in existing feature-based methods, culminating in substantial performance improvement. Code is available at https://github.com/AdvML-Group/SMP-Attack.
Visual Mamba networks (ViMs) extend the selective state space model (Mamba) to various vision tasks and demonstrate significant potential. As a promising compression technique, vector quantization (VQ) decomposes network weights into codebooks and assignments, significantly reducing memory usage and computational latency, thereby enabling the deployment of ViMs on edge devices. Although existing VQ methods have achieved extremely low-bit quantization (e.g., 3-bit, 2-bit, and 1-bit) in convolutional neural networks and Transformer-based networks, directly applying these methods to ViMs results in unsatisfactory accuracy. We identify several key challenges: 1) The weights of Mamba-based blocks in ViMs contain numerous outliers, significantly amplifying quantization errors. 2) When applied to ViMs, the latest VQ methods suffer from excessive memory consumption, lengthy calibration procedures, and suboptimal performance in the search for optimal codewords. In this paper, we propose ViM-VQ, an efficient post-training vector quantization method tailored for ViMs. ViM-VQ consists of two innovative components: 1) a fast convex combination optimization algorithm that efficiently updates both the convex combinations and the convex hulls to search for optimal codewords, and 2) an incremental vector quantization strategy that incrementally confirms optimal codewords to mitigate truncation errors. Experimental results demonstrate that ViM-VQ achieves state-of-the-art performance in low-bit quantization across various visual tasks.
SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers
PDF ↗Vision transformers (ViTs) have become essential backbones in advanced computer vision applications and multi-modal foundation models. Despite their strengths, ViTs remain vulnerable to adversarial perturbations, comparable to or even exceeding the vulnerability of convolutional neural networks (CNNs). Furthermore, the large parameter count and complex architecture of ViTs make them particularly prone to adversarial overfitting, often compromising both clean and adversarial accuracy. This paper mitigates adversarial overfitting in ViTs through a novel, layer-selective fine-tuning approach: SAFER. Instead of optimizing the entire model, we identify and selectively fine-tune a small subset of layers most susceptible to overfitting, applying sharpness-aware minimization to these layers while freezing the rest of the model. Our method consistently enhances both clean and adversarial accuracy over baseline approaches. Typical improvements are around 5%, with some cases achieving gains as high as 20% across various ViT architectures and datasets.
ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
PDF ↗Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.
Enhancing Transferability of Targeted Adversarial Examples via Inverse Target Gradient Competition and Spatial Distance Stretching
PDF ↗In the field of AI security, deep neural networks (DNNs) are highly sensitive to adversarial examples (AEs), which can cause incorrect predictions with minimal input perturbations. Although AEs exhibit transferability across models, targeted attack success rates (TASRs) are low due to differences in feature dimensions and decision boundaries. To enhance targeted AE transferability, we propose a novel approach using Inverse Target Gradient Competition (ITC) and Spatial Distance Stretching (SDS) in the optimization process. Specifically, we employ a siamese-network-like framework to generate both non-targeted and targeted AEs. The ITC mechanism applies non-targeted adversarial gradients each epoch to impede the optimization of targeted perturbations, thereby improving robustness. Additionally, a top-k SDS strategy guides AEs to penetrate target class regions in the latent space while distancing from non-targeted regions, achieving optimal transferability. Compared to state-of-the-art competition-based attacks, our method significantly improves transferable TASRs by 16.1% and 21.4% on mainstream CNNs and ViTs, respectively, and demonstrates superior defense-breaking capabilities.
Conventional backdoor attacks on deep neural networks (DNNs) typically assume that an attacker can manipulate the training data or process. However, recent research introduces a more practical threat model by injecting backdoors at the inference stage. These approaches leverage bit flip attacks to modify model weights using memory fault injection techniques such as Rowhammer. Despite their effectiveness, they suffer from a significant limitation---the need to flip a relatively large number of bits simultaneously, which is highly challenging in practice. To overcome this constraint, we propose SOLEFLIP, the first one-bit-flip backdoor attack on quantized models. Unlike prior methods that rely on optimization-based bit searches and require flipping multiple bits, our algorithm identifies a promising weight for the attack and flips a single bit to insert a backdoor. We evaluate SOLEFLIP on CIFAR-10, SVHN, and ImageNet across various DNN architectures, including a vision transformer. The results show that SOLEFLIP achieves high attack success rates (up to 99.9%, with an average of 98.9%) while causing minimal degradation to benign accuracy (0.0% on average). Furthermore, SOLEFLIP is resilient to backdoor defenses. Our findings reveal a critical threat to DNNs: flipping just one bit in quantized models is sufficient to execute a successful backdoor attack.
Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques seek a common descent direction to benefit all tasks, conventional empirical loss minimization still leaves models prone to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms. thus improve generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness.
Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their deployment on resource-constrained devices, preventing their widespread adoption. In this paper, we present an optimal transport-based method to reduce the depth of over-parametrized deep neural networks, alleviating their computational burden. More specifically, we propose a new regularization strategy based on the Max-Sliced Wasserstein distance to minimize the distance between the intermediate feature distributions in the neural network. We show that minimizing this distance enables the complete removal of intermediate layers in the network, achieving better performance/depth trade-off compared to existing techniques. We assess the effectiveness of our method on traditional image classification setups and extend it to generative image models. Our code is available at https://github.com/VGCQ/LaCoOT.