PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise
Fuente:
arXiv
Saved in:
| Main Authors: | Meng, Qinglong, Xia, Chongkun, Wang, Xueqian |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision Checking
by: Meng, Qinglong, et al.
Published: (2024)
by: Meng, Qinglong, et al.
Published: (2024)
Spectral Norm of Convolutional Layers with Circular and Zero Paddings
by: Delattre, Blaise, et al.
Published: (2024)
by: Delattre, Blaise, et al.
Published: (2024)
Bidirectional Normalizing Flow: From Data to Noise and Back
by: Lu, Yiyang, et al.
Published: (2025)
by: Lu, Yiyang, et al.
Published: (2025)
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models
by: Toker, Michael, et al.
Published: (2025)
by: Toker, Michael, et al.
Published: (2025)
The Coupling Within: Flow Matching via Distilled Normalizing Flows
by: Berthelot, David, et al.
Published: (2026)
by: Berthelot, David, et al.
Published: (2026)
Normalizing Flows are Capable Generative Models
by: Zhai, Shuangfei, et al.
Published: (2024)
by: Zhai, Shuangfei, et al.
Published: (2024)
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation
by: Lee, Chang Won, et al.
Published: (2024)
by: Lee, Chang Won, et al.
Published: (2024)
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
by: Kruse, Mathis, et al.
Published: (2025)
by: Kruse, Mathis, et al.
Published: (2025)
Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling
by: Davtyan, Aram, et al.
Published: (2026)
by: Davtyan, Aram, et al.
Published: (2026)
AlphaFlow: Understanding and Improving MeanFlow Models
by: Zhang, Huijie, et al.
Published: (2025)
by: Zhang, Huijie, et al.
Published: (2025)
NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows
by: Tarasov, Denis, et al.
Published: (2025)
by: Tarasov, Denis, et al.
Published: (2025)
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
by: Yao, Xincheng, et al.
Published: (2024)
by: Yao, Xincheng, et al.
Published: (2024)
Simple ReFlow: Improved Techniques for Fast Flow Models
by: Kim, Beomsu, et al.
Published: (2024)
by: Kim, Beomsu, et al.
Published: (2024)
MixFlow: Mixed Source Distributions Improve Rectified Flows
by: Nayal, Nazir, et al.
Published: (2026)
by: Nayal, Nazir, et al.
Published: (2026)
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
by: Gu, Jiatao, et al.
Published: (2025)
by: Gu, Jiatao, et al.
Published: (2025)
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
by: Shen, Ying, et al.
Published: (2026)
by: Shen, Ying, et al.
Published: (2026)
Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models
by: Chen, Wei, et al.
Published: (2024)
by: Chen, Wei, et al.
Published: (2024)
Real-time Prediction of Urban Sound Propagation with Conditioned Normalizing Flows
by: Eckerle, Achim, et al.
Published: (2025)
by: Eckerle, Achim, et al.
Published: (2025)
Self-Supervised Multi-Frame Neural Scene Flow
by: Liu, Dongrui, et al.
Published: (2024)
by: Liu, Dongrui, et al.
Published: (2024)
Matérn Noise for Triangulation-Agnostic Flow Matching on Meshes
by: Kuai, Tianshu, et al.
Published: (2026)
by: Kuai, Tianshu, et al.
Published: (2026)
Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
by: Mammadov, Abbas, et al.
Published: (2026)
by: Mammadov, Abbas, et al.
Published: (2026)
Improved Mean Flows: On the Challenges of Fastforward Generative Models
by: Geng, Zhengyang, et al.
Published: (2025)
by: Geng, Zhengyang, et al.
Published: (2025)
Block Flow: Learning Straight Flow on Data Blocks
by: Wang, Zibin, et al.
Published: (2025)
by: Wang, Zibin, et al.
Published: (2025)
Learning Straight Flows: Variational Flow Matching for Efficient Generation
by: Ma, Chenrui, et al.
Published: (2025)
by: Ma, Chenrui, et al.
Published: (2025)
Inference-time Stochastic Refinement of GRU-Normalizing Flow for Real-time Video Motion Transfer
by: Haque, Tasmiah, et al.
Published: (2025)
by: Haque, Tasmiah, et al.
Published: (2025)
Jet: A Modern Transformer-Based Normalizing Flow
by: Kolesnikov, Alexander, et al.
Published: (2024)
by: Kolesnikov, Alexander, et al.
Published: (2024)
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
by: Jiang, Zehua, et al.
Published: (2026)
by: Jiang, Zehua, et al.
Published: (2026)
PatchFlow: Leveraging a Flow-Based Model with Patch Features
by: Zhang, Boxiang, et al.
Published: (2026)
by: Zhang, Boxiang, et al.
Published: (2026)
Improving the Training of Rectified Flows
by: Lee, Sangyun, et al.
Published: (2024)
by: Lee, Sangyun, et al.
Published: (2024)
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
by: Gu, Jiatao, et al.
Published: (2025)
by: Gu, Jiatao, et al.
Published: (2025)
Consistency-Aware Padding for Incomplete Multi-Modal Alignment Clustering Based on Self-Repellent Greedy Anchor Search
by: Ma, Shubin, et al.
Published: (2025)
by: Ma, Shubin, et al.
Published: (2025)
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
by: Nagar, Sandeep, et al.
Published: (2024)
by: Nagar, Sandeep, et al.
Published: (2024)
Fast & Efficient Normalizing Flows and Applications of Image Generative Models
by: Nagar, Sandeep
Published: (2025)
by: Nagar, Sandeep
Published: (2025)
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation
by: Sun, Yiyang, et al.
Published: (2024)
by: Sun, Yiyang, et al.
Published: (2024)
ZeroFlow: Scalable Scene Flow via Distillation
by: Vedder, Kyle, et al.
Published: (2023)
by: Vedder, Kyle, et al.
Published: (2023)
MixerFlow: MLP-Mixer meets Normalising Flows
by: English, Eshant, et al.
Published: (2023)
by: English, Eshant, et al.
Published: (2023)
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
by: Bartosh, Grigory, et al.
Published: (2024)
by: Bartosh, Grigory, et al.
Published: (2024)
The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation
by: Cheng, Ho Kei, et al.
Published: (2025)
by: Cheng, Ho Kei, et al.
Published: (2025)
SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
by: Lin, Yexiong, et al.
Published: (2026)
by: Lin, Yexiong, et al.
Published: (2026)
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
by: Cao, Qinglong, et al.
Published: (2024)
by: Cao, Qinglong, et al.
Published: (2024)
Similar Items
-
ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision Checking
by: Meng, Qinglong, et al.
Published: (2024) -
Spectral Norm of Convolutional Layers with Circular and Zero Paddings
by: Delattre, Blaise, et al.
Published: (2024) -
Bidirectional Normalizing Flow: From Data to Noise and Back
by: Lu, Yiyang, et al.
Published: (2025) -
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models
by: Toker, Michael, et al.
Published: (2025) -
The Coupling Within: Flow Matching via Distilled Normalizing Flows
by: Berthelot, David, et al.
Published: (2026)