Deep unfolding network for image super-resolution
Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.
Github: https://github.com/cszn/USRNet
Paper: https://arxiv.org/pdf/2003.10428.pdf
Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.
Github: https://github.com/cszn/USRNet
Paper: https://arxiv.org/pdf/2003.10428.pdf
Improved Techniques for Training Single-Image GANs
The latest convolutional layers are trained with a given learning rate, while previously existing convolutional layers are trained with a smaller learning rate
https://www.tobiashinz.com/2020/03/24/improved-techniques-for-training-single-image-gans.html
Code: https://github.com/tohinz/ConSinGAN
Paper: https://arxiv.org/abs/2003.11512
The latest convolutional layers are trained with a given learning rate, while previously existing convolutional layers are trained with a smaller learning rate
https://www.tobiashinz.com/2020/03/24/improved-techniques-for-training-single-image-gans.html
Code: https://github.com/tohinz/ConSinGAN
Paper: https://arxiv.org/abs/2003.11512
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
Github: https://github.com/facebookresearch/moco
Paper: https://arxiv.org/abs/1911.05722
Github: https://github.com/facebookresearch/moco
Paper: https://arxiv.org/abs/1911.05722
GitHub
GitHub - facebookresearch/moco: PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722 - facebookresearch/moco
New dataset from Google
The Taskmaster-2 dataset consists of 17,289 dialogs
https://research.google/tools/datasets/taskmaster-2/
The Taskmaster-2 dataset consists of 17,289 dialogs
https://research.google/tools/datasets/taskmaster-2/
iTAML: An Incremental Task-Agnostic Meta-learning Approach
iTAML hypothesizes that generalization is a key factor for continual learning Code is implemented using PyTorch and it includes code for running the incremental learning domain experiments
Code: https://github.com/brjathu/iTAML
Paper: https://arxiv.org/abs/2003.11652v1
iTAML hypothesizes that generalization is a key factor for continual learning Code is implemented using PyTorch and it includes code for running the incremental learning domain experiments
Code: https://github.com/brjathu/iTAML
Paper: https://arxiv.org/abs/2003.11652v1
🎲 Probabilistic Regression for Visual Tracking
A general python framework for training and running visual object trackers, based on PyTorch.
Code: https://github.com/visionml/pytracking
Paper: https://arxiv.org/abs/2003.12565
A general python framework for training and running visual object trackers, based on PyTorch.
Code: https://github.com/visionml/pytracking
Paper: https://arxiv.org/abs/2003.12565
Introducing the Model Garden for TensorFlow 2
Code examples for state-of-the-art models and reusable modeling libraries for TensorFlow 2.
https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html
Model Garden repository: https://github.com/tensorflow/models/tree/master/official
Code examples for state-of-the-art models and reusable modeling libraries for TensorFlow 2.
https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html
Model Garden repository: https://github.com/tensorflow/models/tree/master/official
Improving Audio Quality in Duo with WaveNetEQ
https://ai.googleblog.com/2020/04/improving-audio-quality-in-duo-with.html
https://ai.googleblog.com/2020/04/improving-audio-quality-in-duo-with.html
research.google
Improving Audio Quality in Duo with WaveNetEQ
Posted by Pablo Barrera, Software Engineer, Google Research and Florian Stimberg, Research Engineer, DeepMind Online calls have become an everyda...
Flows for simultaneous manifold learning and density estimation
A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
Code: https://github.com/johannbrehmer/manifold-flow
Paper: https://arxiv.org/abs/2003.13913
A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
Code: https://github.com/johannbrehmer/manifold-flow
Paper: https://arxiv.org/abs/2003.13913
Introduction to Quantization on PyTorch
Quantization refers to techniques for doing both computations and memory accesses with lower precision data, usually int8 compared to floating point implementations.
https://pytorch.org/blog/introduction-to-quantization-on-pytorch/
PYTORCH TUTORIALS: https://pytorch.org/tutorials/#model-optimization
Quantization refers to techniques for doing both computations and memory accesses with lower precision data, usually int8 compared to floating point implementations.
https://pytorch.org/blog/introduction-to-quantization-on-pytorch/
PYTORCH TUTORIALS: https://pytorch.org/tutorials/#model-optimization
Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
Simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
Github: https://github.com/SPFlow/SPFlow
Paper: https://arxiv.org/abs/2004.01167v1
Simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
Github: https://github.com/SPFlow/SPFlow
Paper: https://arxiv.org/abs/2004.01167v1
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⚪️ Tracking Objects as Points
Simultaneous object detection and tracking using center points
Github: https://github.com/xingyizhou/CenterTrack
Paper: https://arxiv.org/abs/2004.01177v1
Simultaneous object detection and tracking using center points
Github: https://github.com/xingyizhou/CenterTrack
Paper: https://arxiv.org/abs/2004.01177v1
kNN classification using Neighbourhood Components Analysis
NCA allows you to learn a linear transformation of your data that maximizes k-nearest neighbours performance.
https://kevinzakka.github.io/2020/02/10/nca/
PyTorch Code : https://github.com/kevinzakka/nca
Paper: https://www.cs.toronto.edu/~hinton/absps/nca.pdf
NCA allows you to learn a linear transformation of your data that maximizes k-nearest neighbours performance.
https://kevinzakka.github.io/2020/02/10/nca/
PyTorch Code : https://github.com/kevinzakka/nca
Paper: https://www.cs.toronto.edu/~hinton/absps/nca.pdf
GANSpace: Discovering Interpretable GAN Controls
Simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day.
Code: https://github.com/harskish/ganspace
Paper: https://arxiv.org/abs/2004.02546v1
Video: https://www.youtube.com/watch?v=jdTICDa_eAI&feature=youtu.be
Simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day.
Code: https://github.com/harskish/ganspace
Paper: https://arxiv.org/abs/2004.02546v1
Video: https://www.youtube.com/watch?v=jdTICDa_eAI&feature=youtu.be
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Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html
Code and Pretrained-Models: https://github.com/google-research/simclr
Papaer: https://arxiv.org/abs/2002.05709
https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html
Code and Pretrained-Models: https://github.com/google-research/simclr
Papaer: https://arxiv.org/abs/2002.05709
ML Code Completeness Checklist
https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501
Tips for Publishing Research Code: https://github.com/paperswithcode/releasing-research-code
Facebook blog: https://ai.facebook.com/blog/new-code-completeness-checklist-and-reproducibility-updates/
https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501
Tips for Publishing Research Code: https://github.com/paperswithcode/releasing-research-code
Facebook blog: https://ai.facebook.com/blog/new-code-completeness-checklist-and-reproducibility-updates/
Medium
ML Code Completeness Checklist
Collated best practices from most popular ML research repositories — used for code submissions at NeurIPS 2020.
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