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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🖌 Edit Everything: A Text-Guided Generative System for Images Editing

A text-guided generative system without any finetuning (zero-shot).

🖥 Github: https://github.com/defengxie/edit_everything

Paper: https://arxiv.org/abs/2304.14006v1

🚀 Dataset: https://paperswithcode.com/dataset/wukong

https://news.1rj.ru/str/DataScienceT
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🖥 Awesome Chatgpt

Awesome list for ChatGPT — an artificial intelligence chatbot

🖥 Github: https://github.com/sindresorhus/awesome-chatgpt

💨 Examples: https://github.com/xiaowuc2/ChatGPT-Python-Applications

✅️ QuickGPT: https://sindresorhus.gumroad.com/l/quickgpt

https://news.1rj.ru/str/DataScienceT
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We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free

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ML Research Hub pinned Deleted message
🦠 Learning Protein Representations via Complete 3D Graph Networks

DIG: Dive into Graphs is a turnkey library for graph deep learning research.

Github: https://github.com/divelab/DIG

Paper: https://arxiv.org/abs/2207.12600v1

Tutorials: https://diveintographs.readthedocs.io/en/latest/tutorials/graphdf.html

Documentation: https://diveintographs.readthedocs.io/

Benchmarks: https://github.com/divelab/DIG/tree/dig-stable/benchmarks

Dataset: https://paperswithcode.com/dataset/atom3d

https://news.1rj.ru/str/DataScienceT
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🔄 Caption Anything: Interactive Image Denoscription with Diverse Multimodal Controls


Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences.

🖥 Github: https://github.com/ttengwang/caption-anything

Paper: https://arxiv.org/abs/2305.02677v1

📌 Dataset: https://paperswithcode.com/dataset/cityscapes-3d

🖥 Colab: https://colab.research.google.com/github/ttengwang/Caption-Anything/blob/main/notebooks/tutorial.ipynb

https://news.1rj.ru/str/DataScienceT
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ZipIt! Merging Models from Different Tasks without Training

ZipIt allows to combine completely distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training.

🖥 Github: https://github.com/gstoica27/zipit

Paper: https://arxiv.org/abs/2305.03053v1

📌 Dataset: https://paperswithcode.com/dataset/nabirds

https://news.1rj.ru/str/DataScienceT
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🔈Text-to-Video: The Task, Challenges and the Current State

In this post, we covered the constraints, unique challenges and the current state of text-to-video generation models

🤗 Hugging face: https://huggingface.co/blog/text-to-video

🖥 Github: https://github.com/huggingface/blog/blob/main/text-to-video.md

Damo-vilab: https://huggingface.co/damo-vilab

📌 Dataset: https://m-bain.github.io/webvid-dataset/

https://news.1rj.ru/str/DataScienceT
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🔥 ImageBind: One Embedding Space To Bind Them All

ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data.

🖥 Github: https://github.com/facebookresearch/imagebind

Ⓜ️ Meta blog: https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/

Paper: https://arxiv.org/pdf/2305.05665v1.pdf

⭐️ Demo: https://imagebind.metademolab.com/

📌 Dataset: https://paperswithcode.com/dataset/msr-vtt

https://news.1rj.ru/str/DataScienceT
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Forwarded from Machine Learning
🔰 REST APIs with Flask and Python in 2023

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Link channel:
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📖 DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

A novel self-supervised method for learning dense 3D facial geometry (ie, depth) from face videos, without requiring camera parameters and 3D geometry annotations in training.

🖥 Github: https://github.com/harlanhong/cvpr2022-dagan

Paper: https://arxiv.org/pdf/2305.06225v1.pdf

⭐️ Demo: https://huggingface.co/spaces/HarlanHong/DaGAN

📌 Dataset: https://paperswithcode.com/dataset/voxceleb1

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