1. ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
    Yingjun Du, Zehao Xiao, Shengcai Liao, Cees Snoek
    arXiv 2023. Paper  
    2023-06-26
    2023-06-26
  2. Masked Diffusion Models are Fast Learners
    Jiachen Lei, Peng Cheng, Zhongjie Ba, Kui Ren
    arXiv 2023. Paper  
    2023-06-20
    2023-06-20
  3. Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning
    Michael Tang, David Shustin
    arXiv 2023. Paper  
    2023-06-19
    2023-06-19
  4. The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models
    Roy Voetman, Maya Aghaei, Klaas Dijkstra
    arXiv 2023. Paper  
    2023-06-16
    2023-06-16
  5. When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework
    Jingyi Zhou, Jiamu Sheng, Jiayuan Fan, Peng Ye, Tong He, Bin Wang, Tao Chen
    arXiv 2023. Paper  
    2023-06-15
    2023-06-15
  6. DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
    Tal Daniel, Aviv Tamar
    arXiv 2023. Paper  
    2023-06-09
    2023-06-09
  7. ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
    Changyao Tian, Chenxin Tao, Jifeng Dai, Hao Li, Ziheng Li, Lewei Lu, Xiaogang Wang, Hongsheng Li, Gao Huang, Xizhou Zhu
    arXiv 2023. Paper  
    2023-06-08
    2023-06-08
  8. Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
    Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras
    arXiv 2023. Paper  
    2023-06-02
    2023-06-02
  9. DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
    Sitian Shen, Zilin Zhu, Linqian Fan, Harry Zhang, Xinxiao Wu
    arXiv 2023. Paper  
    2023-05-25
    2023-05-25
  10. Training on Thin Air: Improve Image Classification with Generated Data
    Yongchao Zhou, Hshmat Sahak, Jimmy Ba
    arXiv 2023. Paper   Project   Github  
    2023-05-24
    2023-05-24
  11. Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
    Zheng Li, Yuxuan Li, Penghai Zhao, Renjie Song, Xiang Li, Jian Yang
    arXiv 2023. Paper   Github  
    2023-05-22
    2023-05-22
  12. Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model
    Jie Yang, Bingliang Li, Fengyu Yang, Ailing Zeng, Lei Zhang, Ruimao Zhang
    arXiv 2023. Paper  
    2023-05-20
    2023-05-20
  13. Meta-DM: Applications of Diffusion Models on Few-Shot Learning
    Wentao Hu, Xiurong Jiang, Jiarun Liu, Yuqi Yang, Hui Tian
    arXiv 2023. Paper  
    2023-05-14
    2023-05-14
  14. Class-Balancing Diffusion Models
    Yiming Qin, Huangjie Zheng, Jiangchao Yao, Mingyuan Zhou, Ya Zhang
    CVPR 2023. Paper  
    2023-04-30
    2023-04-30
  15. OVTrack: Open-Vocabulary Multiple Object Tracking
    Siyuan Li, Tobias Fischer, Lei Ke, Henghui Ding, Martin Danelljan, Fisher Yu
    arXiv 2023. Paper  
    2023-04-17
    2023-04-17
  16. Synthetic Data from Diffusion Models Improves ImageNet Classification
    Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet
    arXiv 2023. Paper  
    2023-04-17
    2023-04-17
  17. Your Diffusion Model is Secretly a Zero-Shot Classifier
    Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
    arXiv 2023. Paper   Project  
    2023-03-28
    2023-03-28
  18. Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection
    Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Guennemann
    arXiv 2023. Paper  
    2023-03-27
    2023-03-27
  19. Text-to-Image Diffusion Models are Zero-Shot Classifiers
    Kevin Clark, Priyank Jaini
    arXiv 2023. Paper  
    2023-03-27
    2023-03-27
  20. CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
    Jordan J. Bird, Ahmad Lotfi
    arXiv 2023. Paper  
    2023-03-24
    2023-03-24
  21. Denoising Diffusion Autoencoders are Unified Self-supervised Learners
    Weilai Xiang, Hongyu Yang, Di Huang, Yunhong Wang
    arXiv 2023. Paper  
    2023-03-17
    2023-03-17
  22. Boosting Zero-shot Classification with Synthetic Data Diversity via Stable Diffusion
    Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, Clinton Fookes
    arXiv 2023. Paper  
    2023-02-07
    2023-02-07
  23. Fake it till you make it: Learning(s) from a synthetic ImageNet clone
    Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis
    CVPR 2023. Paper   Project  
    2022-12-16
    2022-12-16
  24. DiffAlign : Few-shot learning using diffusion based synthesis and alignment
    Aniket Roy, Anshul Shah, Ketul Shah, Anirban Roy, Rama Chellappa
    arXiv 2022. Paper  
    2022-12-11
    2022-12-11
  25. Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection
    Luping Liu, Yi Ren, Xize Cheng, Zhou Zhao
    arXiv 2022. Paper   Github  
    2022-11-21
    2022-11-21
  26. DiffusionDet: Diffusion Model for Object Detection
    Shoufa Chen, Peize Sun, Yibing Song, Ping Luo
    arXiv 2022. Paper   Github  
    2022-11-17
    2022-11-17
  27. Denoising Diffusion Models for Out-of-Distribution Detection
    Mark S. Graham, Walter H.L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
    arXiv 2022. Paper   Github  
    2022-11-14
    2022-11-14
  28. A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
    Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan
    arXiv 2022. Paper  
    2022-10-30
    2022-10-30
  29. From Points to Functions: Infinite-dimensional Representations in Diffusion Models
    Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou
    arXiv 2022. Paper   Github  
    2022-10-25
    2022-10-25
  30. Boomerang: Local sampling on image manifolds using diffusion models
    Lorenzo Luzi, Ali Siahkoohi, Paul M Mayer, Josue Casco-Rodriguez, Richard Baraniuk
    arXiv 2022. Paper   Colab  
    2022-10-21
    2022-10-21
  31. Meta-Learning via Classifier(-free) Guidance
    Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe
    arXiv 2022. Paper  
    2022-10-17
    2022-10-17
Counts - 31   Back to top