✨Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment

📝 Summary:
A novel alignment strategy improves Normalizing Flows by aligning their generative reverse pass with vision foundation models. This boosts generative quality, classification accuracy, and training speed, achieving new state-of-the-art results for NFs.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22345
• PDF: https://arxiv.org/pdf/2511.22345
• Github: https://github.com/MCG-NJU/FlowBack

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#NormalizingFlows #GenerativeAI #DeepLearning #ComputerVision #MachineLearning

✨Bidirectional Normalizing Flow: From Data to Noise and Back

📝 Summary:
Bidirectional Normalizing Flow BiFlow improves generative modeling by learning an approximate noise-to-data inverse, removing the need for exact invertibility. This allows flexible architectures, yielding better generation quality and accelerating sampling by up to two orders of magnitude.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10953
• PDF: https://arxiv.org/pdf/2512.10953

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#NormalizingFlows #GenerativeAI #MachineLearning #DeepLearning #DataScience