We introduce HardTVAE, a hardness-aware tabular variational autoencoder designed to improve synthetic data generation in the presence class imbalance. HardTVAE modulates the evidence lower bound with instance-level hardness weights implemented via static, curriculum, or self-paced strategies, and remains agnostic to the choice among the seventeen supported hardness metrics. Complementing the generative component, we propose a multi-view fidelity framework that integrates distributional, topological, complexity-based, and hardness-based perspectives to capture complementary aspects of data fidelity and reveal structural, geometric, and instance-level properties beyond what any single view captures. The multi-view framework is operationalised by fusing the four fidelity views using the harmonic mean to form the Multi-View Fidelity Index (MFI). We evaluate our approach on ten real-world healthcare datasets, a domain where pronounced class imbalance provides a demanding testbed, and validate all comparisons with non-parametric statistical testing. HardTVAE establishes a new performance trade-off compared to both baselines. It significantly exceeds TVAE in downstream utility while still frequently outranking it in fidelity, and it significantly exceeds CTGAN in multi-view fidelity while remaining ahead on average in utility. This advance is realised most consistently by hardness measures such as Class Likelihood (CL), Collective Feature Efficiency (F4), Maximum Individual Feature Efficiency (F3), Tree Depth Unpruned (TDU), Ratio Intra/Extra Class Distance (N2), whose configurations rank above both baselines on fidelity while securing significant utility gains. The proposed generative model and evaluation framework establish a structured basis for both enhancing and evaluating synthetic data under class imbalance.
Generative Adversarial Networks (GANs) have become the backbone of data synthesis across multiple domains like healthcare, audio processing, real-time systems, and federated learning. Despite their revolutionary potential, GANs suffer from major challenges in stability, scalability, and data dependence. This seminar presents a critical and comparative review of ten research papers, each offering a unique solution to the core issues of GAN training instability. The reviewed methods encompass domain adaptation, feature distillation, theoretical regularization, adaptive augmentation, and hierarchical federated architectures. This report provides an in-depth literature analysis, followed by a cross-domain metric-wise performance comparison. The results indicate that no single method dominates across all scenarios, but hybrid combinations lead to the best generalizability and stability under constraints like limited data, non IID settings, or real-time computation. Findings are contextualized in terms of practical impact, with implications for the future design of robust, scalable GAN architectures.
C. Murali, R. Anand· International Journal For Mu...· 0 citations
Equipping a classifier two-sample test with a gradient-boosted discriminator and decomposing it by controlled permutation into marginal, dependency, and numerical-categorical cross components, each read against a fully factorized reference that destroys all dependency while leaving every marginal intact, and against a real-data oracle.
Despite recent advancements in native latent diffusion models for single-view 3D generation, they still suffer from inadequate geometric details due to the global entanglement of VecSet-based representations and inaccurate structural topology arising from single-view ambiguity. Existing solutions often rely on model scaling over large-scale datasets to improve fidelity, which is computationally prohibitive and inefficient in capturing fine-grained details. In this work, we propose Tempo3D, a resource-efficient paradigm for high-fidelity VecSet-based 3D generation via efficient temporal-aware fine-tuning and multi-view latent aggregation. Our approach consists of two critical components: (1) To facilitate robust detail learning, we establish the TempoDetail dataset and design the Time-Segmented LoRA (TS-LoRA) module. TS-LoRA exploits the inherent "structure-to-details" temporal transition of flow-based models to decouple feature learning, significantly bolstering the capacity to produce high-frequency details without large-scale retraining. (2) To mitigate geometric inaccuracies stemming from single-view ambiguity, we propose a multi-view latent aggregation strategy incorporating Soft Flow Trajectory Projection (Soft FTP) and Score-based Velocity Field Aggregation (SVFA). By aggregating reference views derived from Multimodal Large Language Models (MLLMs), these components resolve the intrinsic conflicts between velocity vectors caused by cross-view latent discrepancies, thereby preventing geometric collapse and ensuring directional consistency. Moreover, the integration of MLLMs empowers Tempo3D to support precise semantic 3D editing via natural language prompts while maintaining structural integrity. Experiments demonstrate that Tempo3D outperforms state-of-the-art baselines in generation quality, geometric precision, and editability under a highly efficient computational budget.
Huizhi Zhu, Jiongming Qin, Yusen Wang et al.· ACM Transactions on Graphics· 0 citations
A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.
P. Bhardwaj, Caitlin Jones, Lasse Dierich et al.· Scientific Reports· 2 citations
Tabular data synthesis must satisfy two complementary quality criteria. At the global level, synthetic records must faithfully reproduce the statistical characteristics of the original dataset, including marginal distributions and inter-variable correlations. At the local level, each individual record must satisfy domain-specific logical constraints such as algebraic identities, conditional dependencies, and mutual exclusivity rules. Existing diffusion-based methods excel at global statistical fidelity but frequently violate hard logical constraints, while LLM-based methods offer stronger constraint adherence but suffer from autoregressive distributional drift that degrades global fidelity. In this paper, we propose LLM-Guided Constraint-Aware Tabular Data Synthesis (LCATS), an end-to-end framework that decouples global distribution modeling from local constraint enforcement. LCATS comprises four modules. The Analysis module uses an LLM to discover candidate constraints, encodes them as executable verification scripts, and validates them against the original data to eliminate hallucinated patterns. The Synthesis module employs a diffusion model to generate statistically faithful synthetic data. The Optimization module repairs constraint-violating records through a constrained weighted optimization problem, which dynamically assigns column-level adjustment penalties to minimize distributional distortion. The Evolution module further mitigates distributional distortion through iterative feedback during constraint-aware optimization. Extensive experiments on seven benchmark datasets demonstrate that LCATS achieves strong overall performance against diffusion-based, LLM-based, and hybrid baselines in ?-precision, ?-recall, and downstream predictive performance, while producing zero constraint violations. The code is available at https://github.com/SJTU-DMTai/LCATS.
Qing Li, Yanyan Shen, Qibin Zheng et al.· Proceedings of the 32nd ACM...· 0 citations