Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]

🖥 Github: https://github.com/pietroastolfi/suave-daino

⏩ Paper: https://arxiv.org/pdf/2306.07483v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/imagenet

@Machine_learn

https://www.globaldevelopment.dk/media/attachments/2021/07/31/practical-machine-learning-and-image-processing-1st-edition.pdf

Book: practical machine learning and image processing
year: 2019
Tags: #Data_Science #ML
@Machine_learn

🐼 PandaLM: ReProducible and Automated Language Model Assessment

Judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets.

🖥 Github: https://github.com/weopenml/pandalm

📕 Paper: https://arxiv.org/abs/2306.05087v1

🔗 Dataset: https://github.com/tatsu-lab/stanford_alpaca#data-release

@Machine_learn

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning

🖥 Github: https://github.com/efficienttraining/labelbench

⏩ Paper: https://arxiv.org/pdf/2306.09910v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/cifar-10

@Machine_learn

30 Deep Learning Projects
With Datasets Details
@Machine_learn

CBOGlobalConvergenceAnalysis

🖥 Github: https://github.com/efficienttraining/labelbench

⏩ Paper: https://arxiv.org/pdf/2306.09778v1.pdf

@Machine_learn

🚶‍♂️ 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.


⏩ Project: https://motion-gpt.github.io/

🖥 Github: https://github.com/openmotionlab/motiongpt

📕 Paper: https://arxiv.org/pdf/2306.14795.pdf

🔗Dataset: https://paperswithcode.com/dataset/amass

@Machine_learn

E‏yes estimation and tracking are important research issues in computer vision and human-computer interaction. In this paper, a transfer-based learning model is proposed for this purpose. In the proposed approach, the two ResNet50 networks, whose initial weights are taken from ImageNet, are taught in parallel and finally merged into a layer called feature fusion, the output of the two networks. The proposed approach results show that this approach is better than other approaches on the MPIIGaze dataset. The proposed approach achieved an angle error of 5.83, which resulted in a lower error than other approaches.

با عرض سلام مقاله ی فوق جهت قرار گیری در ارکایو اماده می باشد دوستانی که تمایل به شرکت دارند می تونن به ایدی بنده پیام بدن. جایگاه ۲ و ۳ خالی میباشد.
@Raminmousa

This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

https://www.tg-me.com/DataScienceM
https://www.tg-me.com/DataScienceM

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

🖥 Github: https://github.com/tio-ikim/cellvit

⏩ Paper: https://arxiv.org/pdf/2306.15350v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/pannuke

@Machine_learn

🖥 RL4CO

🖥 Github: https://github.com/kaist-silab/rl4co

📕 Paper: https://arxiv.org/abs/2306.17100v1

🖥 Colab: https://colab.research.google.com/github/kaist-silab/rl4co/blob/main/notebooks/1-quickstart.ipynb

@Machine_learn

Book: Intro to Python® for Computer Science and Data Science
Authors: Paul Deitel • Harvey Deitel
ISBN: 1-292-36490-4
year: 2022
pages: 882
Tags:#Python #Computer_science #Data_Science
@Machine_learn

Book: Graph Data Modeling in Python
Authors: Gary Hutson, Matt Jackson
ISBN: 978-1-80461-803-5
year: 2023
pages: 236
Tags:#Python #Graph #Data_Science
@Machine_learn

📚 Exploratory Data Analysis with Python Cookbook (2023)

1⃣ Join Channel Download:
https://www.tg-me.com/+MhmkscCzIYQ2MmM8

2⃣ Download Book: https://www.tg-me.com/c/1854405158/105

💬 Tags: #Dataanalysis

USEFUL CHANNELS FOR YOU

🏌️ GlOttal-flow LPC Filter (GOLF)

A DDSP-based neural vocoder.


🖥 Github: https://github.com/yoyololicon/golf

📕 Paper: https://arxiv.org/abs/2306.17252v1

🔗Demo: https://yoyololicon.github.io/golf-demo/

@Machine_learn

The Data Science and Python channel is for researchers and advanced programmers

https://www.tg-me.com/DataScienceT

🧬NeuralFuse

🖥 Github: https://github.com/ibm/neuralfuse

⏩ Paper: https://arxiv.org/pdf/2306.16869v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/imagenet

@Machine_learn

🤳Filtered-Guided Diffusion

🖥 Github: https://github.com/jaclyngu/filteredguideddiffusion

⏩ Paper: https://arxiv.org/pdf/2306.17141v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/afhq

@Machine_learn

​
Recognize Anything: A Strong Image Tagging Model

Get ready for a breakthrough in the realm of AI: introducing the Recognize Anything Model (RAM), a powerful new model that is set to revolutionize image tagging. RAM, a titan in the world of large computer vision models, astoundingly exhibits the zero-shot ability to recognize any common category with an impressive level of accuracy. Shattering traditional approaches, RAM employs a unique paradigm for image tagging, utilizing large-scale image-text pairs for training instead of relying on tedious manual annotations.

Paper link: https://arxiv.org/abs/2306.03514
Code link: https://github.com/xinyu1205/recognize-anything
Project link: https://recognize-anything.github.io/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-ram

#deeplearning #cv #imagecaptioning
@Machine_lean

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation ♻️

🖥 Github: https://github.com/pendu/containergym

⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/openai-gym

@Machine_learn