Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]
π₯ Github: https://github.com/Ruixinhua/ExplainableNRS
β© Paper: https://arxiv.org/pdf/2306.07506v1.pdf
π¨ Dataset: https://paperswithcode.com/dataset/mind
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/Ruixinhua/ExplainableNRS
β© Paper: https://arxiv.org/pdf/2306.07506v1.pdf
π¨ Dataset: https://paperswithcode.com/dataset/mind
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β€2β€βπ₯1π1
Multi-Modality Arena
Multi-Modality Arena is an evaluation platform for large multi-modality models.
π₯ Github: https://github.com/opengvlab/multi-modality-arena
βοΈ Demo: https://vlarena.opengvlab.com/
π Paper: https://arxiv.org/abs/2306.09265v1
πDataset: https://paperswithcode.com/dataset/vsr
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Multi-Modality Arena is an evaluation platform for large multi-modality models.
π₯ Github: https://github.com/opengvlab/multi-modality-arena
βοΈ Demo: https://vlarena.opengvlab.com/
π Paper: https://arxiv.org/abs/2306.09265v1
πDataset: https://paperswithcode.com/dataset/vsr
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β€1β€βπ₯1
Get started in Data Science with Microsoft's FREE course for beginners.
- 10 weeks
- 20 lessons
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https://microsoft.github.io/Data-Science-For-Beginners/
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- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE
https://microsoft.github.io/Data-Science-For-Beginners/
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π5β€βπ₯2β€1
Fine-tuning MMS Adapter Models for Multi-Lingual ASR
MMS' Adapter training is both more memory efficient, more robust and yields better performance for low-resource languages.
π€ Post: https://huggingface.co/blog/mms_adapters
π₯ Colab: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_MMS_on_Common_Voice.ipynb
π₯ Github: https://github.com/facebookresearch/fairseq/tree/main/examples/mms/asr
βοΈ Demo: https://huggingface.co/spaces/facebook/MMS
π Paper: https://huggingface.co/papers/2305.13516
https://t.iss.one/DataScienceT
MMS' Adapter training is both more memory efficient, more robust and yields better performance for low-resource languages.
π€ Post: https://huggingface.co/blog/mms_adapters
π₯ Colab: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_MMS_on_Common_Voice.ipynb
π₯ Github: https://github.com/facebookresearch/fairseq/tree/main/examples/mms/asr
βοΈ Demo: https://huggingface.co/spaces/facebook/MMS
π Paper: https://huggingface.co/papers/2305.13516
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β€βπ₯3π3β€1
Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days
https://machinelearningmastery.com/building-transformer-models-with-attention-crash-course-build-a-neural-machine-translator-in-12-days/
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https://machinelearningmastery.com/building-transformer-models-with-attention-crash-course-build-a-neural-machine-translator-in-12-days/
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β€2π2π1
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βοΈ Text-Guided Adversarial Makeup π«£
Novel facial privacy protection via adversarial latent codes. Makeup vs Face Recognition.
π Review: https://t.ly/pBCP
π Paper: arxiv.org/pdf/2306.10008.pdf
π₯ Code: github.com/fahadshamshad/Clip2Protect
https://t.iss.one/DataScienceT
Novel facial privacy protection via adversarial latent codes. Makeup vs Face Recognition.
π Review: https://t.ly/pBCP
π Paper: arxiv.org/pdf/2306.10008.pdf
π₯ Code: github.com/fahadshamshad/Clip2Protect
https://t.iss.one/DataScienceT
π4π1
π Fast Segment Anything
Fast Segment Anything Model reaches comparable performance with the SAM method at 50 times higher run-time speed.
git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
π₯ Github: https://github.com/casia-iva-lab/fastsam
βοΈ Demo:https://huggingface.co/spaces/An-619/FastSAM
π Paper: https://arxiv.org/pdf/2306.12156.pdf
πDataset: https://paperswithcode.com/dataset/sa-1b
https://t.iss.one/DataScienceT
Fast Segment Anything Model reaches comparable performance with the SAM method at 50 times higher run-time speed.
git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
π₯ Github: https://github.com/casia-iva-lab/fastsam
βοΈ Demo:https://huggingface.co/spaces/An-619/FastSAM
π Paper: https://arxiv.org/pdf/2306.12156.pdf
πDataset: https://paperswithcode.com/dataset/sa-1b
https://t.iss.one/DataScienceT
π7β€βπ₯2β€1
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βοΈ LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Extensible and lightweight toolkit, LMFlow, which aims to simplify the finetuning and inference of general large foundation models.
π₯ Github: https://github.com/optimalscale/lmflow
βοΈ Demo: https://lmflow.com/
π Paper: https://arxiv.org/abs/2306.12420v1
πDataset: https://paperswithcode.com/dataset/pubmedqa
Official channel:
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Extensible and lightweight toolkit, LMFlow, which aims to simplify the finetuning and inference of general large foundation models.
π₯ Github: https://github.com/optimalscale/lmflow
βοΈ Demo: https://lmflow.com/
π Paper: https://arxiv.org/abs/2306.12420v1
πDataset: https://paperswithcode.com/dataset/pubmedqa
Official channel:
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CBOGlobalConvergenceAnalysis
π₯ Github: https://github.com/efficienttraining/labelbench
β© Paper: https://arxiv.org/pdf/2306.09778v1.pdf
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π₯ Github: https://github.com/efficienttraining/labelbench
β© Paper: https://arxiv.org/pdf/2306.09778v1.pdf
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β€2
Automatically find issues in image datasets and practice data-centric computer vision.
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!
https://github.com/cleanlab/cleanvision
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CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!
https://github.com/cleanlab/cleanvision
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The Attention Mechanism from Scratch
https://machinelearningmastery.com/the-attention-mechanism-from-scratch/
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Introduction to Computer Architecture, IIT Delhi
π Free Online Course
π» 38 Lecture Videos
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πββοΈ Self paced
Teacher π¨βπ« : Prof. Anshul Kumar
π https://nptel.ac.in/courses/106102062
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π Free Online Course
π» 38 Lecture Videos
β° 1 Module
πββοΈ Self paced
Teacher π¨βπ« : Prof. Anshul Kumar
π https://nptel.ac.in/courses/106102062
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π₯ Awesome-Multimodal-Large-Language-Models
Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.
π₯ Github: https://github.com/bradyfu/awesome-multimodal-large-language-models
π Paper: https://arxiv.org/abs/2306.13394v1
πDataset: https://paperswithcode.com/dataset/coco
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Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.
π₯ Github: https://github.com/bradyfu/awesome-multimodal-large-language-models
π Paper: https://arxiv.org/abs/2306.13394v1
πDataset: https://paperswithcode.com/dataset/coco
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π Machine Learning Cheat Sheets
https://sites.google.com/view/datascience-cheat-sheets
Machine Learning Animations: https://sites.google.com/view/mlingifs#h.341bzfgiuxfx
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Machine Learning Animations: https://sites.google.com/view/mlingifs#h.341bzfgiuxfx
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π2β€βπ₯1β€1
β‘ LightGlue. Local Feature Matching at Light Speed
LightGlue a lightweight feature matcher with high accuracy and adaptive pruning techniques, both in the width and depth of the network, for blazing fast inference.
π₯ Github: https://github.com/cvg/lightglue
π Paper: https://arxiv.org/abs/2306.13643v1
πDataset: https://paperswithcode.com/dataset/hpatches
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LightGlue a lightweight feature matcher with high accuracy and adaptive pruning techniques, both in the width and depth of the network, for blazing fast inference.
git clone https://github.com/cvg/LightGlue.git && cd LightGlue
python -m pip install -e .
π₯ Github: https://github.com/cvg/lightglue
π Paper: https://arxiv.org/abs/2306.13643v1
πDataset: https://paperswithcode.com/dataset/hpatches
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β€2π2
This channel is for Programmers, Coders, Software Engineers.
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πΆββοΈ MotionGPT: Human Motion
as Foreign Language
MotionGPT consists of a motion tokenizer responsible for converting raw motion data into discrete motion tokens, as well as a motion-aware language model that learns to understand the motion tokens from large language pre-training models by corresponding textual descriptions.
β© Project: https://motion-gpt.github.io/
π₯ Github: https://github.com/openmotionlab/motiongpt
π Paper: https://arxiv.org/pdf/2306.14795.pdf
πDataset: https://paperswithcode.com/dataset/amass
https://t.iss.one/DataScienceT
as Foreign Language
MotionGPT consists of a motion tokenizer responsible for converting raw motion data into discrete motion tokens, as well as a motion-aware language model that learns to understand the motion tokens from large language pre-training models by corresponding textual descriptions.
β© Project: https://motion-gpt.github.io/
π₯ Github: https://github.com/openmotionlab/motiongpt
π Paper: https://arxiv.org/pdf/2306.14795.pdf
πDataset: https://paperswithcode.com/dataset/amass
https://t.iss.one/DataScienceT
β€βπ₯2β€2π1