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Introducing Dreamer: Scalable Reinforcement Learning Using World Models
Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination.
https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html
Paper: https://arxiv.org/abs/1912.01603
Blog: https://dreamrl.github.io/
Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination.
https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html
Paper: https://arxiv.org/abs/1912.01603
Blog: https://dreamrl.github.io/
Few-Shot Object Detection (FsDet)
Detecting rare objects from a few examples is an emerging problem.
In addition to the benchmarks we introduce new benchmarks on three datasets: PASCAL VOC, COCO, and LVIS. We sample multiple groups of few-shot training examples for multiple runs of the experiments and report evaluation results on both the base classes and the novel classes.
Github: https://github.com/ucbdrive/few-shot-object-detection
Paper: https://arxiv.org/abs/2003.06957
Detecting rare objects from a few examples is an emerging problem.
In addition to the benchmarks we introduce new benchmarks on three datasets: PASCAL VOC, COCO, and LVIS. We sample multiple groups of few-shot training examples for multiple runs of the experiments and report evaluation results on both the base classes and the novel classes.
Github: https://github.com/ucbdrive/few-shot-object-detection
Paper: https://arxiv.org/abs/2003.06957
Scene Text Recognition via Transformer
The method use a convolutional feature maps as word embedding input into transformer.
Github: https://github.com/fengxinjie/Transformer-OCR
Paper: https://arxiv.org/abs/2003.08077
The transformer source code:https://nlp.seas.harvard.edu/2018/04/03/attention.html
The method use a convolutional feature maps as word embedding input into transformer.
Github: https://github.com/fengxinjie/Transformer-OCR
Paper: https://arxiv.org/abs/2003.08077
The transformer source code:https://nlp.seas.harvard.edu/2018/04/03/attention.html
High-Resolution Daytime Translation Without Domain Labels
HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution.
https://saic-mdal.github.io/HiDT/
Paper: https://arxiv.org/abs/2003.08791
Video: https://www.youtube.com/watch?v=DALQYKt-GJc&feature=youtu.be
HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution.
https://saic-mdal.github.io/HiDT/
Paper: https://arxiv.org/abs/2003.08791
Video: https://www.youtube.com/watch?v=DALQYKt-GJc&feature=youtu.be
PyTorch Tutorial: How to Develop Deep Learning Models with Python
https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/
https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/
NeRF: Neural Radiance Fields
Algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction
https://www.matthewtancik.com/nerf
Tensorflow implementation: https://github.com/bmild/nerf
Paper: https://arxiv.org/abs/2003.08934v1
Algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction
https://www.matthewtancik.com/nerf
Tensorflow implementation: https://github.com/bmild/nerf
Paper: https://arxiv.org/abs/2003.08934v1
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