Implementing Transfer Learning in PyTorch

Fine-tuning and feature extraction with PyTorch

Link: https://medium.com/analytics-vidhya/transfer-learning-in-pytorch-f7736598b1ed

#PyTorch #novice #entrylevel #beginner

Free book on #PyTorch

Book with all the information required to work with one of the most popular #dl frameworks.

Link: https://twitter.com/PyTorch/status/1197603717144432640?s=20

#book #manual

​​Neighbourhood Components Analysis
a PyTorch implementation of Neighbourhood Components Analysis

NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.

The authors propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.

It can also learn low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, this classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them.

The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.

paper (only pdf): https://www.cs.toronto.edu/~hinton/absps/nca.pdf
github: https://github.com/kevinzakka/nca

#kNN #pca #nca #PyTorch

​​StarGAN v2 code release on GitHub

The better news is if you put a human into the animal model you do in fact get out a feline version of the human, and it's even wearing a suit.

GitHub: https://github.com/clovaai/stargan-v2
ArXiV: https://arxiv.org/abs/1912.01865
YouTube: https://www.youtube.com/watch?v=0EVh5Ki4dIY&feature=youtu.be

#GAN #StarGAN #PyTorch

​​Automatic product tagging on photoes on Facebook Pages

#FacebookAI released an improvement aiming at enhancing shopping platform.

Post: https://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences
Paper: https://scontent-arn2-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf

#GrokNet #DL #segmentation #PyTorch

Live U-Net implementation online session today

Famous Abhishek Thakur (First 4x GM on Kaggle) is going to show you how to implement the original U-Net with #PyTorch.

Session starts in 4 hours from now (at 6PM CET / 9.30PM IST), make sure you turned the notifications on if you are interested.

YouTube Link: https://www.youtube.com/watch?v=u1loyDCoGbE

#Livecoding #Unet

​​ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network

The authors propose a set of design principles that improves model performance significantly based on the analysis of representation bottlenecks.

Authors think that commonly used architectures have a representation bottleneck and try to fix it by expanding channel size, using more expand layers, and better activation functions. This also improves the performance of models on ImageNet and good results on transfer learning on classification and object detection.
Authors hope that their design ideas could be used by NAS to create even better models.


Paper: https://arxiv.org/abs/2007.00992
Code: https://github.com/clovaai/rexnet

#deeplearning #pretraining #transferlearning #computervision #pytorch

​​Funnel Activation for Visual Recognition

Authors offer a new activation function for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.

Extensive experiments on COCO, ImageNet and CityScape show significant improvement and robustness.


Paper: https://arxiv.org/abs/2007.11824
Code: https://github.com/megvii-model/FunnelAct

#deeplearning #activationfunction #computervision #pytorch

🏥Self-supervised Learning for Medical images

Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.

GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680

#biolearning #medical #dl #pytorch #keras

Implementing original #UNet paper using #PyTorch

Video tutorial on how to code your own neural network from scratch.

Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597

🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


Ultimate Posts

* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda


Open Data Science

ODS.ai is an international community of people anyhow related to Data Science.

Website: https://ods.ai



Hashtags

Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization


Chats

- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website

ODS resources

* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai

Feedback and Contacts

You are welcome to reach administration through telegram bot: @opendatasciencebot

​​Segment Anything

The Segment Anything project aims to democratize image segmentation in computer vision, a core task used across various applications such as scientific imagery analysis and photo editing. Traditionally, accurate segmentation models require specialized expertise, AI training infrastructure, and large amounts of annotated data. This project introduces a new task, dataset, and model for image segmentation to overcome these challenges and make segmentation more accessible.

The researchers are releasing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date. These resources will enable a wide range of applications and further research into foundational models for computer vision. The SA-1B dataset is available for research purposes, while the SAM is provided under the permissive Apache 2.0 open license. Users can explore the demo to try SAM with their own images.

Paper link: https://arxiv.org/abs/2304.02643

Code link: https://github.com/facebookresearch/segment-anything

Demo link: https://segment-anything.com/demo

Blogpost link: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/

Dataset link: https://ai.facebook.com/datasets/segment-anything/

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

#deeplearning #cv #pytorch #imagesegmentation #dataset

​​DINOv2: Learning Robust Visual Features without Supervision

Get ready for a game-changer in computer vision! Building on the groundbreaking achievements in natural language processing, foundation models are revolutionizing the way we use images in various systems. By generating all-purpose visual features that excel across diverse image distributions and tasks without finetuning, these models are set to redefine the field.

The researchers behind this work have combined cutting-edge techniques to scale pretraining in terms of data and model size, turbocharging the training process like never before. They've devised an ingenious automatic pipeline to create a rich, diverse, and curated image dataset, setting a new standard in the self-supervised literature. To top it off, they've trained a colossal ViT model with a staggering 1 billion parameters and distilled it into a series of smaller, ultra-efficient models. These models outshine the best available all-purpose features, OpenCLIP, on most benchmarks at both image and pixel levels.

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dinov2

Project link: https://dinov2.metademolab.com/
#deeplearning #cv #pytorch #imagesegmentation #sota #pretraining