CLIP: Train Faster with Less Data
Author:
Affiliation:
1. Qatar University,Qatar Mobility Innovations Center (QMIC),Doha,Qatar
2. Qatar University,Electrical Engineering,Doha,Qatar
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10066534/10066540/10066625.pdf?arnumber=10066625
Reference44 articles.
1. Dataset pruning: Reducing training data by examining generalization influence;yang;ArXiv,2022
2. MobileCount: An Efficient Encoder-Decoder Framework for Real-Time Crowd Counting
3. Data Fine-Pruning: A Simple Way to Accelerate Neural Network Training
4. CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting
5. Revisiting crowd counting: State-of-the-art, trends, and future perspectives;khan;ArXiv,2022
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1. Learning Rate Curriculum;International Journal of Computer Vision;2024-07-27
2. Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy;SN Computer Science;2024-06-28
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