PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.
https://shunsukesaito.github.io/PIFu/
Code: https://github.com/shunsukesaito/PIFu
Paper: https://arxiv.org/abs/1905.05172
Method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.
https://shunsukesaito.github.io/PIFu/
Code: https://github.com/shunsukesaito/PIFu
Paper: https://arxiv.org/abs/1905.05172
shunsukesaito.github.io
PIFu
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
TuiGAN can be use for various computer vision tasks ranging from image style transfer to object transformation and appearance transformation.
Github: https://github.com/linjx-ustc1106/TuiGAN-PyTorch
Paper: https://arxiv.org/abs/2004.04634
TuiGAN can be use for various computer vision tasks ranging from image style transfer to object transformation and appearance transformation.
Github: https://github.com/linjx-ustc1106/TuiGAN-PyTorch
Paper: https://arxiv.org/abs/2004.04634
Huawei announced that its TensorFlow and PyTorch-style MindSpore Deep Learning middleware is now open source
https://towardsdatascience.com/huaweis-mindspore-a-new-competitor-for-tensorflow-and-pytorch-d319deff2aec
Github: https://github.com/mindspore-ai/mindspore
Docs: https://www.mindspore.cn/docs/en/0.1.0-alpha/architecture.html
Official tutorials: https://www.mindspore.cn/en
https://towardsdatascience.com/huaweis-mindspore-a-new-competitor-for-tensorflow-and-pytorch-d319deff2aec
Github: https://github.com/mindspore-ai/mindspore
Docs: https://www.mindspore.cn/docs/en/0.1.0-alpha/architecture.html
Official tutorials: https://www.mindspore.cn/en
Medium
Huawei’s MindSpore: A new competitor for TensorFlow and PyTorch?
Huawei announced that its TensorFlow and PyTorch-style MindSpore Deep Learning middleware is now open source. Discover in this post its…
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Nevergrad, an evolutionary optimization platform, adds new key features
Facebook AI’s open source Python3 library for derivative-free and evolutionary optimization.
https://ai.facebook.com/blog/nevergrad-an-evolutionary-optimization-platform-adds-new-key-features/
GitHub: https://github.com/facebookresearch/nevergrad
Documentation: https://facebookresearch.github.io/nevergrad/index.html
Facebook AI’s open source Python3 library for derivative-free and evolutionary optimization.
https://ai.facebook.com/blog/nevergrad-an-evolutionary-optimization-platform-adds-new-key-features/
GitHub: https://github.com/facebookresearch/nevergrad
Documentation: https://facebookresearch.github.io/nevergrad/index.html
EfficientDet from Google: Towards Scalable and Efficient Object Detection
A new family of scalable and efficient object detectors. EfficientDet achieves state-of-the-art accuracy while being up to 9x smaller and using significantly less computation compared to prior state-of-the-art detectors
https://ai.googleblog.com/2020/04/efficientdet-towards-scalable-and.html
Github: https://github.com/google/automl/tree/master/efficientdet
Paper: https://arxiv.org/abs/1911.09070
Tutorial: https://github.com/google/automl/blob/master/efficientdet/tutorial.ipynb
A new family of scalable and efficient object detectors. EfficientDet achieves state-of-the-art accuracy while being up to 9x smaller and using significantly less computation compared to prior state-of-the-art detectors
https://ai.googleblog.com/2020/04/efficientdet-towards-scalable-and.html
Github: https://github.com/google/automl/tree/master/efficientdet
Paper: https://arxiv.org/abs/1911.09070
Tutorial: https://github.com/google/automl/blob/master/efficientdet/tutorial.ipynb
Local-Global Video-Text Interactions for Temporal Grounding
This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query
Github: https://github.com/JonghwanMun/LGI4temporalgrounding
Paper: https://arxiv.org/abs/2004.07514
This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query
Github: https://github.com/JonghwanMun/LGI4temporalgrounding
Paper: https://arxiv.org/abs/2004.07514
👍1
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Announcing PyCaret 1.0.0
An open source low-code machine learning library in Python. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment.
https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46
Habr RU : https://habr.com/ru/company/otus/blog/497770/
Github: https://github.com/pycaret/pycaret
Guide: https://pycaret.org/guide/
An open source low-code machine learning library in Python. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment.
https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46
Habr RU : https://habr.com/ru/company/otus/blog/497770/
Github: https://github.com/pycaret/pycaret
Guide: https://pycaret.org/guide/
Neural Networks from Scratch - Coding a Layer
A beginner’s guide to understanding the inner workings of Deep Learning
https://morioh.com/p/fb1b9f5a52bc
Video Part 1: https://www.youtube.com/watch?v=Wo5dMEP_BbI
Video Part 2: https://www.youtube.com/watch?v=lGLto9Xd7bU
A beginner’s guide to understanding the inner workings of Deep Learning
https://morioh.com/p/fb1b9f5a52bc
Video Part 1: https://www.youtube.com/watch?v=Wo5dMEP_BbI
Video Part 2: https://www.youtube.com/watch?v=lGLto9Xd7bU
Transform and Tell: Entity-Aware News Image Captioning
End-to-end model which generates captions for images embedded in news articles.
Github: https://github.com/alasdairtran/transform-and-tell
Demo: https://transform-and-tell.ml/
Paper: https://arxiv.org/abs/2004.08070
End-to-end model which generates captions for images embedded in news articles.
Github: https://github.com/alasdairtran/transform-and-tell
Demo: https://transform-and-tell.ml/
Paper: https://arxiv.org/abs/2004.08070
Today, on April 22 is Earth day. It’s a right time to look at the climate issues in terms of data storage.
* 90% of all data was created in the last two years
* IoT, Big Data and AI are huge data creators
* 70% of all data stored is copy data2
* 70-80% of data is typically unstructured * 2018 HDD shipments = 869Eb
* 2023 HDD shipments = 2.6Zb
* In a normal DC, 1 watt of HDD consumption = 1 watt of cooling
What can everyone do for the ecology of our planet?
* Migrate suitable workloads to the cloud
* Collect, process and store less data; archive more to reduce carbon storage
* Use backup/archive instead of big data
* Leverage copy management tools
* If you must keep data for longer, use tape or cloud tape
Software solutions for backup, managing and recovering data help to move your data to the cloud and so you can take care of the environment. Commvault - leading experts in software-defined storage. Over 11 Exabytes of customer data are under Commvault management.
* 90% of all data was created in the last two years
* IoT, Big Data and AI are huge data creators
* 70% of all data stored is copy data2
* 70-80% of data is typically unstructured * 2018 HDD shipments = 869Eb
* 2023 HDD shipments = 2.6Zb
* In a normal DC, 1 watt of HDD consumption = 1 watt of cooling
What can everyone do for the ecology of our planet?
* Migrate suitable workloads to the cloud
* Collect, process and store less data; archive more to reduce carbon storage
* Use backup/archive instead of big data
* Leverage copy management tools
* If you must keep data for longer, use tape or cloud tape
Software solutions for backup, managing and recovering data help to move your data to the cloud and so you can take care of the environment. Commvault - leading experts in software-defined storage. Over 11 Exabytes of customer data are under Commvault management.
ResNeSt: Split-Attention Networks
Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.
Github: https://github.com/zhanghang1989/ResNeSt#pretrained-models
Paper: https://arxiv.org/abs/2004.08955v1
Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.
Github: https://github.com/zhanghang1989/ResNeSt#pretrained-models
Paper: https://arxiv.org/abs/2004.08955v1
How to Develop an Extra Trees Ensemble with Python
https://machinelearningmastery.com/extra-trees-ensemble-with-python/
https://machinelearningmastery.com/extra-trees-ensemble-with-python/
MachineLearningMastery.com
How to Develop an Extra Trees Ensemble with Python - MachineLearningMastery.com
Extra Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees.
It is related to the widely used random forest algorithm. It can often achieve as-good or better performance than the random forest algorithm…
It is related to the widely used random forest algorithm. It can often achieve as-good or better performance than the random forest algorithm…
The Illustrated GPT-2 (Visualizing Transformer Language Models)
Visual explaining the inner-workings of transformers, and how they’ve evolved since the original paper
https://jalammar.github.io/illustrated-gpt2/
Habr ru: https://habr.com/ru/post/490842/
OpenAI Implementation: https://github.com/openai/gpt-2
Visual explaining the inner-workings of transformers, and how they’ve evolved since the original paper
https://jalammar.github.io/illustrated-gpt2/
Habr ru: https://habr.com/ru/post/490842/
OpenAI Implementation: https://github.com/openai/gpt-2
Training with quantization noise for extreme model compression
Quant-Noise is a new technique to enable extreme compression of models that still deliver high performance when deployed in practical applications.
https://ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression/
Paper: https://arxiv.org/abs/2004.07320
GitHub: https://github.com/pytorch/fairseq/tree/master/examples/quant_noise
Quant-Noise is a new technique to enable extreme compression of models that still deliver high performance when deployed in practical applications.
https://ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression/
Paper: https://arxiv.org/abs/2004.07320
GitHub: https://github.com/pytorch/fairseq/tree/master/examples/quant_noise
Building a Real Time Emotion Detection with Python
https://morioh.com/p/801c509dda99
Code: https://github.com/Dhanush45/Realtime-emotion-detectionusing-python
https://morioh.com/p/801c509dda99
Code: https://github.com/Dhanush45/Realtime-emotion-detectionusing-python
How I taught my computer to play Spot it! using OpenCV and Deep Learning
https://towardsdatascience.com/how-i-learned-my-computer-to-play-spot-it-using-opencv-and-deep-learning-ad1f017a3ec3
Habr ru: https://habr.com/ru/company/otus/blog/498800/
Code: https://github.com/henniedeharder/spotit/tree/master/DeepLearningSpotIt
https://towardsdatascience.com/how-i-learned-my-computer-to-play-spot-it-using-opencv-and-deep-learning-ad1f017a3ec3
Habr ru: https://habr.com/ru/company/otus/blog/498800/
Code: https://github.com/henniedeharder/spotit/tree/master/DeepLearningSpotIt
Towards Data Science
How I taught my computer to play Spot it! using OpenCV and Deep Learning | Towards Data Science
Some fun with computer vision and CNNs with a small dataset.
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
Easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models.
Code: https://github.com/UKPLab/sentence-transformers
Paper: https://arxiv.org/abs/2004.09813v1
Easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models.
Code: https://github.com/UKPLab/sentence-transformers
Paper: https://arxiv.org/abs/2004.09813v1
NVIDIA Announce MONAI Open Source AI Framework for Healthcare Research
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging.
https://blogs.nvidia.com/blog/2020/04/21/monai-open-source-framework-ai-healthcare/?ncid=so-twit-79443#cid=ix11_so-twit_en-us
Code: https://github.com/Project-MONAI/MONAI
Docs: https://monai.readthedocs.io/en/latest/
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging.
https://blogs.nvidia.com/blog/2020/04/21/monai-open-source-framework-ai-healthcare/?ncid=so-twit-79443#cid=ix11_so-twit_en-us
Code: https://github.com/Project-MONAI/MONAI
Docs: https://monai.readthedocs.io/en/latest/
NVIDIA Blog
NVIDIA Blogs: PyTorch-Based Project Aids Researchers Developing AI in Healthcare
Open-source AI framework for healthcare builds on the best practices from existing tools, including NVIDIA Clara, NiftyNet, DLTK and DeepNeuro.
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Google’s Optimizing Multiple Loss Functions with Loss-Conditional Training
https://ai.googleblog.com/2020/04/optimizing-multiple-loss-functions-with.html
Demo: https://sites.google.com/view/stochss/adjusting-parameters
ADJUSTABLE REAL-TIME STYLE TRANSFER: https://sites.google.com/view/stochss/adjusting-parameters
https://ai.googleblog.com/2020/04/optimizing-multiple-loss-functions-with.html
Demo: https://sites.google.com/view/stochss/adjusting-parameters
ADJUSTABLE REAL-TIME STYLE TRANSFER: https://sites.google.com/view/stochss/adjusting-parameters
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🦑 Нейроэволюция киберкальмаров
Для создания нейронных сетей, обеспечивающих поведение без обучения, можно использовать нейроэволюцию. Эволюционные алгоритмы (например, такой, который я использовал для выполнения эволюции растений) подвергают генетический код эволюции в течение долгого периода времени. Генетический код (модель для ДНК) и представляемый им организм изначально очень просты, но в течение многих поколений небольшие мутации увеличивают благоприятную сложность и добавляют функции, стимулирующие дальнейшее распространение этих свойств.
Цифровые кальмары
Чтобы продемонстрировать действие нейроэволюции, я хочу подвергнуть эволюции цифровых кальмаров. Кальмары обладают следующими свойствами:
➡️ Читать дальше :
🔩 Код из статьи
@ai_machinelearning_big_data
Для создания нейронных сетей, обеспечивающих поведение без обучения, можно использовать нейроэволюцию. Эволюционные алгоритмы (например, такой, который я использовал для выполнения эволюции растений) подвергают генетический код эволюции в течение долгого периода времени. Генетический код (модель для ДНК) и представляемый им организм изначально очень просты, но в течение многих поколений небольшие мутации увеличивают благоприятную сложность и добавляют функции, стимулирующие дальнейшее распространение этих свойств.
Цифровые кальмары
Чтобы продемонстрировать действие нейроэволюции, я хочу подвергнуть эволюции цифровых кальмаров. Кальмары обладают следующими свойствами:
@ai_machinelearning_big_data
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