Good summary article about GANs
https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/
https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/
MachineLearningMastery.com
Best Resources for Getting Started With GANs - MachineLearningMastery.com
Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization…
Nice paper on disentanglement representation(where each dimension of embedding represent one feature)
https://proceedings.mlr.press/v97/mathieu19a.html
https://proceedings.mlr.press/v97/mathieu19a.html
PMLR
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilm...
Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
https://github.com/videoflow/videoflow
https://github.com/videoflow/videoflow
GitHub
GitHub - videoflow/videoflow: Python framework that facilitates the quick development of complex video analysis applications and…
Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. - videoflow/videoflow
Generative Adversarial Networks: A Survey and Taxonomy
By @wangvilla @sheqi1991 @tomasward
7 architecture-variant GANs and 9 loss-variant GANs focusing on
(1) High quality image generation
(2) Diverse image generation
(3) Stable training
https://arxiv.org/abs/1906.01529v1
By @wangvilla @sheqi1991 @tomasward
7 architecture-variant GANs and 9 loss-variant GANs focusing on
(1) High quality image generation
(2) Diverse image generation
(3) Stable training
https://arxiv.org/abs/1906.01529v1
arXiv.org
Generative Adversarial Networks: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably the revolutionary techniques are in the area of
computer vision such as plausible image...
past few years. Arguably the revolutionary techniques are in the area of
computer vision such as plausible image...
"Likelihood Ratios for Out-of-Distribution Detection"
Ren et al.: https://arxiv.org/abs/1906.02845
#artificialintelligence #deeplearning #machinelearning
Ren et al.: https://arxiv.org/abs/1906.02845
#artificialintelligence #deeplearning #machinelearning
TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization
Blog by TensorFlow: https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-post-training-integer-quantization-b4964a1ea9ba
#tensorflow #artificialintelligence#naturallanguageprocessing
Blog by TensorFlow: https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-post-training-integer-quantization-b4964a1ea9ba
#tensorflow #artificialintelligence#naturallanguageprocessing
Medium
TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization
Since we introduced the Model Optimization Toolkit — a suite of techniques that both novice and advanced developers can use to optimize…
Invertible Residual Networks
Official Pytorch implementation of i-ResNets.
By Jorn Jacobsen: https://github.com/jhjacobsen/invertible-resnet
#pytorch #technology #machinelearning
Official Pytorch implementation of i-ResNets.
By Jorn Jacobsen: https://github.com/jhjacobsen/invertible-resnet
#pytorch #technology #machinelearning
"Deep Learning for Cognitive Neuroscience" https://arxiv.org/abs/1903.01458
@ArtificialIntelligenceArticles
@ArtificialIntelligenceArticles
arXiv.org
Deep Learning for Cognitive Neuroscience
Neural network models can now recognise images, understand text, translate languages, and play many human games at human or superhuman levels. These systems are highly abstracted, but are inspired...
SLIDES
Evaluating Deep Generative Models on Out-of-Distribution Inputs
Eric NalisnickOxCSML Seminar 31.5.19
https://people.ds.cam.ac.uk/etn22/nalisnick_OxCSML_talk.pdf
Evaluating Deep Generative Models on Out-of-Distribution Inputs
Eric NalisnickOxCSML Seminar 31.5.19
https://people.ds.cam.ac.uk/etn22/nalisnick_OxCSML_talk.pdf
SLIDES
ICML Tutorial on Population-Based Methods for Training Deep Neural Networks: Novelty Search, Quality Diversity, Open-Ended Search Algorithms, & Indirect Encoding
https://www.cs.uwyo.edu/~jeffclune/share/2019_06_10_ICML_Tutorial.pdf
ICML Tutorial on Population-Based Methods for Training Deep Neural Networks: Novelty Search, Quality Diversity, Open-Ended Search Algorithms, & Indirect Encoding
https://www.cs.uwyo.edu/~jeffclune/share/2019_06_10_ICML_Tutorial.pdf
"Machine learning approach for low-dose CT imaging yields superior results" via Nature Machine Intelligence
https://m.phys.org/news/2019-06-machine-approach-low-dose-ct-imaging.html
https://m.phys.org/news/2019-06-machine-approach-low-dose-ct-imaging.html
phys.org
Machine learning approach for low-dose CT imaging yields superior results
Machine learning has the potential to vastly advance medical imaging, particularly computerized tomography (CT) scanning, by reducing radiation exposure and improving image quality.
Facebook's machine learning system MelNet generated this AI voice clone of Bill Gates. Others voice clones can be heard here: https://audio-samples.github.io under the heading “Selected Speakers.”
ICML 2019 | Google, ETH Zurich, MPI-IS, Cambridge & PROWLER.io Share Best Paper Honours
https://medium.com/syncedreview/icml-2019-google-eth-zurich-mpi-is-cambridge-prowler-io-share-best-paper-honours-4aeabd5c9fc8
https://medium.com/syncedreview/icml-2019-google-eth-zurich-mpi-is-cambridge-prowler-io-share-best-paper-honours-4aeabd5c9fc8
Medium
ICML 2019 | Google, ETH Zurich, MPI-IS, Cambridge & PROWLER.io Share Best Paper Honours
The 36th International Conference on Machine Learning (ICML) kicked off Monday in California. The ICML is one of the world’s two top…
Energy-Based Models applied to the detection of machine-generated text, as opposed to human-produced text.
Brought to you from FAIR.
https://arxiv.org/abs/1906.03351
Brought to you from FAIR.
https://arxiv.org/abs/1906.03351
arXiv.org
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing...
Are you really into object recognition, but you are sick of looking at 2D boxes and 2D masks? Let's play with 3D shapes!
We build on Mask R-CNN and extend it to infer 3D meshes. Given an input image, we detect all objects, infer their 2D instance boxes and masks as well as their 3D object shapes, all end-to-end! Naturally, we call our approach Mesh R-CNN :D
Paper: https://arxiv.org/abs/1906.02739
Joint work with Jitendra Malik and Justin Johnson
P.S. This task is really hard!
We build on Mask R-CNN and extend it to infer 3D meshes. Given an input image, we detect all objects, infer their 2D instance boxes and masks as well as their 3D object shapes, all end-to-end! Naturally, we call our approach Mesh R-CNN :D
Paper: https://arxiv.org/abs/1906.02739
Joint work with Jitendra Malik and Justin Johnson
P.S. This task is really hard!
arXiv.org
Mesh R-CNN
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world....
Here are the COMPLETE Lecture notes on Professor Andrew Ng's
Stanford Machine Learning Lecture: https://www.holehouse.org/mlclass/
Stanford Machine Learning Lecture: https://www.holehouse.org/mlclass/
Tons of presentations from Embedded Vision Summit 2019
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/downloads/pages/may-2019-summit-slides
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/downloads/pages/may-2019-summit-slides
Edge AI and Vision Alliance
May 2019 Embedded Vision Summit Replay
Keynotes “Making the Invisible Visible: Within Our Bodies, the World Around Us and Beyond,” a Keynote Presentation from the MIT Media Lab June
Recent paper from CVPR'19 paper about neural free-viewpoint rendering of human avatars without reconstructing geometry
https://www.profillic.com/paper/arxiv:1905.08776
https://www.profillic.com/paper/arxiv:1905.08776
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…