Generative Models

On Calibration of Speech Classification Models: Insights from Energy-Based Model Investigations Interspeech 2024 On conditional diffusion models for PDE simulations NeurIPS 2024 On Convergence in Wasserstein Distance and f-divergence Minimization Problems AISTATS 2024 On Divergence Measures for Training GFlowNets NeurIPS 2024 One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion CVPR 2024 One-Shot Structure-Aware Stylized Image Synthesis CVPR 2024 One-step Diffusion with Distribution Matching Distillation CVPR 2024 One Style Is All You Need To Generate a Video WACV 2024 On Exact Inversion of DPM-Solvers CVPR 2024 On Mechanistic Knowledge Localization in Text-to-Image Generative Models ICML 2024 On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection ICLR 2024 On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution NeurIPS 2024 On the Posterior Distribution in Denoising: Application to Uncertainty Quantification ICLR 2024 On the Quantification of Image Reconstruction Uncertainty Without Training Data WACV 2024 On the Role of Edge Dependency in Graph Generative Models ICML 2024 On the Stability of Iterative Retraining of Generative Models on their own Data ICLR 2024 On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows ICML 2024 OpenBias: Open-set Bias Detection in Text-to-Image Generative Models CVPR 2024 Operationalizing Essential Characteristics of Creativity in a Computational System for Music Composition AAAI 2024 Optimal Budgeted Rejection Sampling for Generative Models AISTATS 2024 Optimal Eye Surgeon: Finding image priors through sparse generators at initialization ICML 2024 Optimal Flow Matching: Learning Straight Trajectories in Just One Step NeurIPS 2024 Optimal score estimation via empirical Bayes smoothing COLT 2024 Optimization Can Learn Johnson Lindenstrauss Embeddings NeurIPS 2024 Optimizing Diffusion Noise Can Serve As Universal Motion Priors CVPR 2024