{"total":83,"rows":[{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781,"area":"diffusion"},{"id":"2112.07804","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","created":"2021-12-15","cited_by_count":516,"area":"diffusion"},{"id":"2103.09396","title":"Pros and Cons of GAN Evaluation Measures: New Developments","created":"2021-03-17","cited_by_count":123,"area":null},{"id":"2205.12524","title":"Accelerating Diffusion Models via Early Stop of the Diffusion Process","created":"2022-05-25","cited_by_count":100,"area":"diffusion"},{"id":"2310.09336","title":"Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task","created":"2023-10-13","cited_by_count":93,"area":"diffusion"},{"id":"2303.05762","title":"TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets","created":"2023-03-10","cited_by_count":88,"area":"diffusion"},{"id":"2309.05569","title":"ITI-GEN: Inclusive Text-to-Image Generation","created":"2023-09-11","cited_by_count":64,"area":null},{"id":"2210.00586","title":"Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2","created":"2022-10-02","cited_by_count":55,"area":"diffusion"},{"id":"2304.01814","title":"CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization","created":"2023-04-04","cited_by_count":52,"area":"diffusion"},{"id":"2102.05113","title":"Negative Data Augmentation","created":"2021-02-09","cited_by_count":46,"area":null},{"id":"2304.09991","title":"Supporting Human-AI Collaboration in Auditing LLMs with LLMs","created":"2023-04-19","cited_by_count":46,"area":null},{"id":"2210.00482","title":"Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language","created":"2022-10-02","cited_by_count":42,"area":null},{"id":"2303.05470","title":"Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases","created":"2023-03-09","cited_by_count":41,"area":null},{"id":"2102.01331","title":"Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder","created":"2021-02-02","cited_by_count":39,"area":null},{"id":"2310.05400","title":"Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers","created":"2023-10-09","cited_by_count":33,"area":null},{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2408.04591","title":"HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts","created":"2024-08-08","cited_by_count":23,"area":null},{"id":"2202.11455","title":"On PAC-Bayesian reconstruction guarantees for VAEs","created":"2022-02-23","cited_by_count":20,"area":null},{"id":"2306.14969","title":"On the Sample Complexity of Quantum Boltzmann Machine Learning","created":"2023-06-26","cited_by_count":19,"area":null},{"id":"2109.05826","title":"Variational Disentanglement for Domain Generalization","created":"2021-09-13","cited_by_count":18,"area":null},{"id":"2303.07735","title":"Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models","created":"2023-03-14","cited_by_count":18,"area":"lr"},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2106.02923","title":"Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization","created":"2021-06-05","cited_by_count":17,"area":null},{"id":"2108.01285","title":"Toward Spatially Unbiased Generative Models","created":"2021-08-03","cited_by_count":17,"area":null},{"id":"2512.05927","title":"World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertainty","created":"2025-12-05","cited_by_count":17,"area":"wm"},{"id":"2302.10916","title":"How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study","created":"2023-02-17","cited_by_count":16,"area":null},{"id":"2303.15626","title":"A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models","created":"2023-03-27","cited_by_count":16,"area":null},{"id":"2302.05259","title":"Star-Shaped Denoising Diffusion Probabilistic Models","created":"2023-02-10","cited_by_count":13,"area":"diffusion"},{"id":"2408.02464","title":"Fairness and Bias Mitigation in Computer Vision: A Survey","created":"2024-08-05","cited_by_count":13,"area":null},{"id":"2408.13256","title":"How Diffusion Models Learn to Factorize and Compose","created":"2024-08-23","cited_by_count":13,"area":"diffusion"},{"id":"2104.05077","title":"CoPE: Conditional image generation using Polynomial Expansions","created":"2021-04-11","cited_by_count":12,"area":null},{"id":"2106.02953","title":"Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases","created":"2021-06-05","cited_by_count":12,"area":null},{"id":"2301.13083","title":"Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off","created":"2023-01-30","cited_by_count":12,"area":null},{"id":"2309.16421","title":"Distilling ODE Solvers of Diffusion Models into Smaller Steps","created":"2023-09-28","cited_by_count":11,"area":"diffusion"},{"id":"2311.01018","title":"Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning","created":"2023-11-02","cited_by_count":11,"area":"diffusion"},{"id":"2403.11340","title":"StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining","created":"2024-03-17","cited_by_count":11,"area":null},{"id":"2205.11640","title":"Generalization Gap in Amortized Inference","created":"2022-05-23","cited_by_count":10,"area":null},{"id":"2212.00261","title":"Task Discovery: Finding the Tasks that Neural Networks Generalize on","created":"2022-12-01","cited_by_count":9,"area":null},{"id":"2312.05579","title":"Conditional Stochastic Interpolation for Generative Learning","created":"2023-12-09","cited_by_count":9,"area":null},{"id":"2110.06990","title":"Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers","created":"2021-10-13","cited_by_count":8,"area":null},{"id":"2201.08770","title":"Generalization Metrics for Practical Quantum Advantage in Generative Models","created":"2022-01-21","cited_by_count":8,"area":null},{"id":"2111.00780","title":"Pseudo-Spherical Contrastive Divergence","created":"2021-11-01","cited_by_count":7,"area":null},{"id":"2310.07131","title":"Echocardiography video synthesis from end diastolic semantic map via diffusion model","created":"2023-10-11","cited_by_count":7,"area":"diffusion"},{"id":"2408.08307","title":"What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models","created":"2024-08-15","cited_by_count":7,"area":"diffusion"},{"id":"2410.21273","title":"On Inductive Biases That Enable Generalization of Diffusion Transformers","created":"2024-10-28","cited_by_count":7,"area":"diffusion"},{"id":"2202.04040","title":"Self-Conditioned Generative Adversarial Networks for Image Editing","created":"2022-02-08","cited_by_count":6,"area":null},{"id":"2205.04730","title":"Power of Quantum Generative Learning","created":"2022-05-10","cited_by_count":6,"area":null},{"id":"2210.09509","title":"Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach","created":"2022-10-18","cited_by_count":6,"area":"diffusion"},{"id":"2305.18063","title":"Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization","created":"2023-05-29","cited_by_count":6,"area":null},{"id":"2311.09064","title":"Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models","created":"2023-11-15","cited_by_count":6,"area":"wm"},{"id":"2402.04383","title":"FairWire: Fair Graph Generation","created":"2024-02-06","cited_by_count":6,"area":null},{"id":"2212.02090","title":"Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling","created":"2022-12-05","cited_by_count":5,"area":null},{"id":"2310.18291","title":"Addressing GAN Training Instabilities via Tunable Classification Losses","created":"2023-10-27","cited_by_count":5,"area":null},{"id":"2405.19201","title":"Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations","created":"2024-05-29","cited_by_count":5,"area":"diffusion"},{"id":"2502.04038","title":"Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents","created":"2025-02-06","cited_by_count":5,"area":null},{"id":"2111.00909","title":"Multi-Attribute Balanced Sampling for Disentangled GAN Controls","created":"2021-10-28","cited_by_count":4,"area":null},{"id":"2210.10418","title":"Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation","created":"2022-10-19","cited_by_count":4,"area":null},{"id":"2303.15161","title":"Data Augmentation for Environmental Sound Classification Using Diffusion Probabilistic Model with Top-k Selection Discriminator","created":"2023-03-27","cited_by_count":4,"area":"diffusion"},{"id":"2310.00277","title":"A Unified Framework for Generative Data Augmentation: A Comprehensive Survey","created":"2023-09-30","cited_by_count":4,"area":null},{"id":"2402.03305","title":"Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?","created":"2024-02-05","cited_by_count":4,"area":"diffusion"},{"id":"2405.13977","title":"Improving Fairness and Mitigating MADness in Generative Models","created":"2024-05-22","cited_by_count":4,"area":null},{"id":"2408.08184","title":"Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion","created":"2024-08-15","cited_by_count":4,"area":"diffusion"},{"id":"2501.18797","title":"Compositional Generalization via Forced Rendering of Disentangled Latents","created":"2025-01-30","cited_by_count":4,"area":null},{"id":"2212.06142","title":"Towards Better Long-range Time Series Forecasting using Generative Forecasting","created":"2022-12-09","cited_by_count":3,"area":null},{"id":"2410.02246","title":"PFGuard: A Generative Framework with Privacy and Fairness Safeguards","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2410.13136","title":"Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance","created":"2024-10-17","cited_by_count":3,"area":"diffusion"},{"id":"2508.02903","title":"RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation","created":"2025-08-04","cited_by_count":3,"area":"diffusion"},{"id":"2410.13964","title":"Sparse Mixture-of-Experts for Compositional Generalization: Empirical Evidence and Theoretical Foundations of Optimal Sparsity","created":"2024-10-17","cited_by_count":2,"area":"moe"},{"id":"2509.21470","title":"Score-based Idempotent Distillation of Diffusion Models","created":"2025-09-25","cited_by_count":2,"area":"diffusion"},{"id":"2103.12165","title":"Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy","created":"2021-03-22","cited_by_count":1,"area":null},{"id":"2302.00788","title":"A supplemental investigation of non-linearity in quantum generative models with respect to simulatability and optimization","created":"2023-02-01","cited_by_count":1,"area":null},{"id":"2407.11108","title":"SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation","created":"2024-07-15","cited_by_count":1,"area":"diffusion"},{"id":"2408.13850","title":"Condensed Data Expansion Using Model Inversion for Knowledge Distillation","created":"2024-08-25","cited_by_count":1,"area":"distill"},{"id":"2412.17505","title":"More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models","created":"2024-12-23","cited_by_count":1,"area":null},{"id":"2412.19780","title":"Tensor Network Estimation of Distribution Algorithms","created":"2024-12-27","cited_by_count":1,"area":null},{"id":"2603.17041","title":"When Marginals Match but Structure Fails: Covariance Fidelity in Generative Models","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2110.08770","title":"Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks","created":"2021-10-17","cited_by_count":0,"area":null},{"id":"2209.10948","title":"Implementing and Experimenting with Diffusion Models for Text-to-Image Generation","created":"2022-09-22","cited_by_count":0,"area":"diffusion"},{"id":"2305.02865","title":"CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing","created":"2023-05-04","cited_by_count":0,"area":null},{"id":"2505.09831","title":"IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2505.19313","title":"Concept Reachability in Diffusion Models: Beyond Dataset Constraints","created":"2025-05-25","cited_by_count":0,"area":"diffusion"},{"id":"2605.15734","title":"Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2607.11941","title":"GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization","created":"2026-07-11","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}