ML Research Hub – Telegram
ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting

🖥 Github: https://github.com/junwuzhang19/repaint123

📕 Paper: https://arxiv.org/pdf/2312.13271v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/nerf

https://news.1rj.ru/str/DataScienceT
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🌠AnyDoor: Zero-shot Object-level Image Customization

pip install git+https://github.com/cocodataset/panopticapi.git

pip install pycocotools -i https://pypi.douban.com/simple

pip install lvis


🖥 Code: https://github.com/damo-vilab/AnyDoor

🎓 HF: https://huggingface.co/spaces/xichenhku/AnyDoor-online

🔮 Project Page: https://damo-vilab.github.io/AnyDoor-Page/

📚 ArXiv: https://arxiv.org/abs/2307.09481

https://news.1rj.ru/str/DataScienceT
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The first channel in the world of Telegram is dedicated to helping students and programmers of artificial intelligence, machine learning and data science in obtaining data sets for their research.

https://news.1rj.ru/str/datasets1
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🍏Ferret: Refer and Ground Anything Anywhere at Any Granularity

🖥 Code: github.com/apple/ml-ferret

🎓 Paper: https://arxiv.org/abs/2310.07704

https://news.1rj.ru/str/DataScienceT
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

🖥 Github: https://github.com/OpenDriveLab/LaneSegNet

📕 Paper: https://arxiv.org/abs/2312.16108v1

🔥Datasets: https://paperswithcode.com/dataset/openlane-v2

https://news.1rj.ru/str/DataScienceT
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🌹4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

🖥 Code: https://github.com/VITA-Group/4DGen

🔮 Project: https://vita-group.github.io/4DGen/

📚 ArXiv: https://arxiv.org/abs/2305.06456
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✔️ AIJack: Security and Privacy Risk Simulator for Machine Learning

Security and Privacy Risk Simulator for Machine Learning.

pip install git+https://github.com/Koukyosyumei/AIJack

🖥 Code: https://github.com/microsoft/promptbench

🌟 Docs: https://promptbench.readthedocs.io/en/latest/

📚 Paper: https://arxiv.org/abs/2312.07910v1

⚡️ Dataset: https://paperswithcode.com/dataset/mmlu

https://news.1rj.ru/str/DataScienceT
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