Artificial Intelligence && Deep Learning
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Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers

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Artificial Intelligence && Deep Learning pinned «🌟 Join the AI Revolution with AiNews.com! 🌟 πŸš€ Meet Michael, the driving force behind AiNews.com, straight from sunny California! 🌞 πŸ‘©β€πŸ’» Discover the latest and greatest in Pure AI News and exclusive, FREE AI products. πŸ“ˆ Be a part of our ever-growing subscriber…»
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StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation

paper
: https://arxiv.org/pdf/2312.12491v1.pdf

source code: https://github.com/cumulo-autumn/streamdiffusion?tab=readme-ov-file
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This channels is for Programmers, Coders, Software Engineers.

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PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

[Paper] [Project Page] [Model Card]
[πŸ€— Demo (Realistic)] [πŸ€— Demo (Stylization)]

🌠 Key Features:
1. Rapid customization within seconds, with no additional LoRA training.
2. Ensures impressive ID fidelity, offering diversity, promising text controllability, and high-quality generation.
3. Can serve as an Adapter to collaborate with other Base Models alongside LoRA modules in community.
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Depth Anything
Unleashing the Power of Large-Scale Unlabeled Data

[
Paper] [Code] [Demo]

join our community
:
πŸ‘‰ @deeplearning_ai
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MLOps Masterclass

πŸ”₯Closing registration soon!

Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy

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+918940876397 / +918778033930
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Awesome-AIGC-3D

A curated list of awesome AIGC 3D papers, inspired by awesome-NeRF.

Source code: https://github.com/hitcslj/awesome-aigc-3d?tab=readme-ov-file

join our community:
πŸ‘‰ @deeplearning_ai
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EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction

1. Channel: @deeplearning_ai

2.Source Code: https://github.com/mit-han-lab/efficientvit

3. Paper: https://arxiv.org/abs/2402.05008
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πŸ†”πŸ†” Magic-Me: Identity-Specific Video πŸ†”πŸ†”

πŸ‘‰hashtag#ByteDance (+UC Berkeley) unveils VCD for video-gen: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt. Impressive results, source code under Apache 2.0 πŸ’™

𝐇𝐒𝐠𝐑π₯𝐒𝐠𝐑𝐭𝐬:
βœ…Novel Video Custom Diffusion (VCD) framework
βœ…High-Quality ID-specific videos generation
βœ…Improvement in aligning IDs-images and text
βœ…Robust 3D Gaussian Noise Prior for denoising
βœ…Better Inter-frame correlation / video consistency
βœ…New modules F-VCD/T-VCD for videos upscale
βœ…New train with masked loss by prompt-to-segmentation

hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse

πŸ‘‰Channel: @deeplearning_ai
πŸ‘‰Paper https://arxiv.org/pdf/2402.09368.pdf
πŸ‘‰Project https://magic-me-webpage.github.io/
πŸ‘‰Code https://github.com/Zhen-Dong/Magic-Me
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Result.gif
23.1 MB
🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!

Features
:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets

Watch the performance showcase on GitHub for future advancements.

[Source Code] [Paper]

join our community:
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Navigating the Landscape of MLOps & LLMOps πŸš€

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🌟 What you'll gain:
1️⃣ ML model deployment techniques on AWS, Azure, GCP & open source.
2️⃣ Efficient data management insights.
3️⃣ Explore the latest MLOps tools.
4️⃣ Real-time interaction with expert instructors.

🚩 Limited spots available! Don't miss out!

πŸ‘‰ Enroll now:
https://bit.ly/mlops-webinar

πŸ‘₯ Share with fellow ML enthusiasts! πŸš€βœ¨
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7️⃣ Deep Learning
8️⃣ programming Languages

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Introducing ECoDepth: The New Benchmark in Diffusive Mono-Depth

From the labs of IITD, we unveil ECoDepth - our groundbreaking SIDE model powered by a diffusion backbone and enriched with ViT embeddings. This innovation sets a new standard in single image depth estimation (SIDE), offering unprecedented accuracy and semantic understanding.

Key Features:

βœ…Revolutionary MDE approach tailored for SIDE tasks
βœ…Enhanced semantic context via ViT embeddings
βœ…Superior performance in zero-shot transfer tasks
βœ…Surpasses previous SOTA models by up to 14%

Dive into the future of depth estimation with ECoDepth. Access our source code and explore the full potential of our model.

πŸ“– Read the Paper
πŸ’» Get the Code

#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse

join our community:
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