Code accompanying the NeurIPS 2020 oral paper
Network-to-Network Translation with Conditional Invertible Neural Networks
Paper: https://compvis.github.io/net2net/
source codes: https://github.com/CompVis/net2net?utm_source=Deep+Learning+Weekly&utm_campaign=b85cddf829-EMAIL_CAMPAIGN_2019_04_24_03_18_COPY_01&utm_medium=email&utm_term=0_384567b42d-b85cddf829-156293989
join: https://t.me/DeepLearning_ai
Network-to-Network Translation with Conditional Invertible Neural Networks
Paper: https://compvis.github.io/net2net/
source codes: https://github.com/CompVis/net2net?utm_source=Deep+Learning+Weekly&utm_campaign=b85cddf829-EMAIL_CAMPAIGN_2019_04_24_03_18_COPY_01&utm_medium=email&utm_term=0_384567b42d-b85cddf829-156293989
join: https://t.me/DeepLearning_ai
Real-Time Lane Detection and alerts for Autonomous Driving
medium: https://towardsdatascience.com/real-time-lane-detection-and-alerts-for-autonomous-driving-1f0a021390ee
paper: https://arxiv.org/pdf/1807.01726.pdf
github repo: https://github.com/MaybeShewill-CV/lanenet-lane-detection
Join: https://t.me/DeepLearning_ai
medium: https://towardsdatascience.com/real-time-lane-detection-and-alerts-for-autonomous-driving-1f0a021390ee
paper: https://arxiv.org/pdf/1807.01726.pdf
github repo: https://github.com/MaybeShewill-CV/lanenet-lane-detection
Join: https://t.me/DeepLearning_ai
Medium
Real-Time Lane Detection and alerts for Autonomous Driving
Doing cool things with data
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
https://deepai.org/publication/fast-and-furious-real-time-end-to-end-3d-detection-tracking-and-motion-forecasting-with-a-single-convolutional-net
Join: https://t.me/DeepLearning_ai
https://deepai.org/publication/fast-and-furious-real-time-end-to-end-3d-detection-tracking-and-motion-forecasting-with-a-single-convolutional-net
Join: https://t.me/DeepLearning_ai
DeepAI
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion
Forecasting with a Single Convolutional Net
Forecasting with a Single Convolutional Net
12/22/20 - In this paper we propose a novel deep neural network that is able to jointly
reason about 3D detection, tracking and motion foreca...
reason about 3D detection, tracking and motion foreca...
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The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
https://www.kdnuggets.com/2021/01/top-10-computer-vision-papers-2020.html
Join: https://t.me/DeepLearning_ai
https://www.kdnuggets.com/2021/01/top-10-computer-vision-papers-2020.html
Join: https://t.me/DeepLearning_ai
KDnuggets
Top 10 Computer Vision Papers 2020
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
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Multi-scale inference is commonly used to improve the results of semantic segmentation. combining multi-scale predictions. it to be roughly 4x more memory efficient to train than other recent approaches. In addition to enabling faster training, this allows us to train with larger crop sizes which leads to greater model accuracy.
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Hierarchical Multi-Scale Attention for Semantic Segmentation
PyTorch implementation
Github: https://github.com/NVIDIA/semantic-segmentation
Video: https://www.youtube.com/watch?v=odAGA7pFBGA&feature=youtu.be
Paper: https://arxiv.org/abs/2005.10821
Method Details: https://www.cityscapes-dataset.com/method-details/?submissionID=7836
PyTorch implementation
Github: https://github.com/NVIDIA/semantic-segmentation
Video: https://www.youtube.com/watch?v=odAGA7pFBGA&feature=youtu.be
Paper: https://arxiv.org/abs/2005.10821
Method Details: https://www.cityscapes-dataset.com/method-details/?submissionID=7836
GitHub
GitHub - NVIDIA/semantic-segmentation: Nvidia Semantic Segmentation monorepo
Nvidia Semantic Segmentation monorepo. Contribute to NVIDIA/semantic-segmentation development by creating an account on GitHub.
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Good day dear subscribers.
Today, 25th January, our community are already more than 30K.
Within these years we learn or still learning more about specific topics through channel.
I try my best to provide, keep going with contemporary knowledge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python.
Thanks for being with us and stay with us and invite your friends (https://t.me/DeepLearning_ai).
If you have suggestions to improve the channel's content or related things, please let me know. Thanks
@ShohruhRakhmatov
Today, 25th January, our community are already more than 30K.
Within these years we learn or still learning more about specific topics through channel.
I try my best to provide, keep going with contemporary knowledge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python.
Thanks for being with us and stay with us and invite your friends (https://t.me/DeepLearning_ai).
If you have suggestions to improve the channel's content or related things, please let me know. Thanks
@ShohruhRakhmatov
Telegram
Artificial Intelligence && Deep Learning
Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact: @ai_adminn
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact: @ai_adminn
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500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲
500 AI Machine learning Deep learning Computer vision NLP Projects with code
This list is continuously updated. - You can take pull request and contribute.
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
500 AI Machine learning Deep learning Computer vision NLP Projects with code
This list is continuously updated. - You can take pull request and contribute.
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
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MIT 6.S191 Introduction to Deep Learning 2021
Course Description MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!
Course Description MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!
Review — AdderNet: Do We Really Need Multiplications in Deep Learning? (Image Classification)
This is a paper in 2020 CVPR with over 20 citations. (
Sik-Ho Tsang @ Medium)
https://sh-tsang.medium.com/review-addernet-do-we-really-need-multiplications-in-deep-learning-image-classification-b72851ddb255
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
This is a paper in 2020 CVPR with over 20 citations. (
Sik-Ho Tsang @ Medium)
https://sh-tsang.medium.com/review-addernet-do-we-really-need-multiplications-in-deep-learning-image-classification-b72851ddb255
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
Medium
Review — AdderNet: Do We Really Need Multiplications in Deep Learning? (Image Classification)
Using Addition Instead of Multiplication for Convolution, Lower Latency Than the Conventional CNN
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ERFNet: Efficient Residual Factorized ConvNet for
Real-time Semantic Segmentation [Cited by 452]
paper:
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
github [PyTorch]:
https://github.com/Eromera/erfnet_pytorch
Real-time Semantic Segmentation [Cited by 452]
paper:
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
github [PyTorch]:
https://github.com/Eromera/erfnet_pytorch
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Awesome Semantic Segmentation
Networks by architecture
* U-Net
* SegNet
* DeepLab
* FCN
* ENet
* LinkNet
* DenseNet
* DilatedNet
* PixelNet
* .....
A sea of semantic segmentation source codes with papers
https://github.com/mrgloom/awesome-semantic-segmentation/blob/master/README.md
JOIN US
Networks by architecture
* U-Net
* SegNet
* DeepLab
* FCN
* ENet
* LinkNet
* DenseNet
* DilatedNet
* PixelNet
* .....
A sea of semantic segmentation source codes with papers
https://github.com/mrgloom/awesome-semantic-segmentation/blob/master/README.md
JOIN US
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Review — PAN: Pyramid Attention Network for Semantic Segmentation (Semantic Segmentation
Using FPA & GAU Modules, Outperforms FCN, DeepLabv2, CRF-RNN, DeconvNet, DPN, PSPNet, DPN, DeepLabv2, RefineNet, DUC, and PSPNet.
https://medium.com/mlearning-ai/review-pan-pyramid-attention-network-for-semantic-segmentation-semantic-segmentation-8d94101ba24a
https://t.me/DeepLearning_ai
Using FPA & GAU Modules, Outperforms FCN, DeepLabv2, CRF-RNN, DeconvNet, DPN, PSPNet, DPN, DeepLabv2, RefineNet, DUC, and PSPNet.
https://medium.com/mlearning-ai/review-pan-pyramid-attention-network-for-semantic-segmentation-semantic-segmentation-8d94101ba24a
https://t.me/DeepLearning_ai
2021- Courses List of Machine Learning, Deep Learning, and Computer Vision from a top school
CS224W: Machine Learning with Graphs - Stanford / Winter 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW
Full Stack Deep Learning - Spring 2021 - UC Berkeley
https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9
Introduction to Deep Learning (I2DL) - Technical University of Munich
https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk
3D Computer Vision - National University of Singapore - 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ
CV3DST - Computer Vision 3: Detection, Segmentation and Tracking
https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe
ADL4CV - Advanced Deep Learning for Computer Vision
https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz
👉https://t.me/DeepLearning_ai
CS224W: Machine Learning with Graphs - Stanford / Winter 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW
Full Stack Deep Learning - Spring 2021 - UC Berkeley
https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9
Introduction to Deep Learning (I2DL) - Technical University of Munich
https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk
3D Computer Vision - National University of Singapore - 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ
CV3DST - Computer Vision 3: Detection, Segmentation and Tracking
https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe
ADL4CV - Advanced Deep Learning for Computer Vision
https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz
👉https://t.me/DeepLearning_ai
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PAPER WITH CODES
https://paperswithcode.com/
The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.
We believe this is best done together with the community, supported by NLP and ML.
Also operate specialized portals for papers with code in astronomy, physics, computer sciences, mathematics and statistics.
#Contributing
Anyone can contribute - look for the "Edit" buttons!
Want to submit a new code implementation? Search for the paper title, and then add the implementation on the paper page
https://paperswithcode.com/
you can find a sea of implemented source code with papers.
👉https://t.me/DeepLearning_ai
https://paperswithcode.com/
The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.
We believe this is best done together with the community, supported by NLP and ML.
Also operate specialized portals for papers with code in astronomy, physics, computer sciences, mathematics and statistics.
#Contributing
Anyone can contribute - look for the "Edit" buttons!
Want to submit a new code implementation? Search for the paper title, and then add the implementation on the paper page
https://paperswithcode.com/
you can find a sea of implemented source code with papers.
👉https://t.me/DeepLearning_ai
Paperswithcode
Papers with Code - The latest in Machine Learning
Papers With Code highlights trending Machine Learning research and the code to implement it.
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