The Pandas Workshop (2022).pdf
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The Pandas Workshop A comprehensive guide to using Python for data analysis with real-world case studies
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Enhance-A-Video: Better Generated Video for Free
11 Feb 2025 · Yang Luo, Xuanlei Zhao, Mengzhao Chen, Kaipeng Zhang, Wenqi Shao, Kai Wang, Zhangyang Wang, Yang You
DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.
Paper: https://arxiv.org/pdf/2502.07508v1.pdf
Code: https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
@Machine_learn
11 Feb 2025 · Yang Luo, Xuanlei Zhao, Mengzhao Chen, Kaipeng Zhang, Wenqi Shao, Kai Wang, Zhangyang Wang, Yang You
DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.
Paper: https://arxiv.org/pdf/2502.07508v1.pdf
Code: https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
@Machine_learn
Bayesian Sample Inference
🖥 Github: https://github.com/martenlienen/bsi
📕 Paper: https://arxiv.org/abs/2502.07580
🌟 Dataset: https://paperswithcode.com/dataset/cifar-10
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🌟 Dataset: https://paperswithcode.com/dataset/cifar-10
@Machine_learn
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نیاز به یک نفر داریم که در موضوع زیرکمکمون کنه (نفر اول)
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Title: Chronic kidney disease classification: Deep ansemble approach
کنفرانس مد نظر :
⭐️ https://saiconference.com/IntelliSys
⚙️ Abstract: Chronic kidney disease (CKD) is a progressive disease that may lead to kidney failure, so early diagnosis is crucial for proper management. This condition has a high mortality rate, especially in developing countries. CKD is often overlooked because there are no apparent symptoms in the early stages. Meanwhile, early diagnosis and timely clinical intervention are essential to reduce the progression of the disease. CKD diagnosis using deep learning (DL) and feature selection (FS) methods can be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnosis (CAD) to assist physicians. DL models are based on automatic feature selection.
In some cases, manual feature extraction can improve the results before the network learning process. This study aims to present an ensemble deep-learning model for CKD classification. The proposed method used Deep Embedded Clustering (DEC) as a similarity feature. Also, latent features obtained from the Gaussian Mixture Model (GMM) process were used. The proposed method on UCI databases achieved an accuracy of 1.0 using the Synthetic Minority Over-Sampling technique (SMOTE).
دوستانی که مشارکت میکنم بخشی از هزینه چاپ رو هم تقبل میکنن. بخش related work and introduction, هم بر عهده ی مشارکت کنندست.
@Raminmousa
Papers channel: https://www.tg-me.com/+SP9l58Ta_zZmYmY0
نیاز به یک نفر داریم که در موضوع زیرکمکمون کنه (نفر اول)
Title: Chronic kidney disease classification: Deep ansemble approach
کنفرانس مد نظر :
In some cases, manual feature extraction can improve the results before the network learning process. This study aims to present an ensemble deep-learning model for CKD classification. The proposed method used Deep Embedded Clustering (DEC) as a similarity feature. Also, latent features obtained from the Gaussian Mixture Model (GMM) process were used. The proposed method on UCI databases achieved an accuracy of 1.0 using the Synthetic Minority Over-Sampling technique (SMOTE).
دوستانی که مشارکت میکنم بخشی از هزینه چاپ رو هم تقبل میکنن. بخش related work and introduction, هم بر عهده ی مشارکت کنندست.
@Raminmousa
Papers channel: https://www.tg-me.com/+SP9l58Ta_zZmYmY0
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