Looks useful: Keras-OCR (3rd-party package) provides out-of-the-box OCR models and an end-to-end training pipeline to build new OCR modelshttps://t.co/q7rN49VjvD
— François Chollet (@fchollet) January 4, 2020
Looks useful: Keras-OCR (3rd-party package) provides out-of-the-box OCR models and an end-to-end training pipeline to build new OCR modelshttps://t.co/q7rN49VjvD
— François Chollet (@fchollet) January 4, 2020
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection https://t.co/2oyMcVP4SX
— Delip Rao (@deliprao) January 4, 2020
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping https://t.co/Wbcdv0iQJk
— /MachineLearning (@slashML) January 2, 2020
"Today’s CNN-generated images (ex: #DeepFakes) retain detectable fingerprints that distinguish them from real photos. This allows forensic classifiers to generalize from one model to another without extensive adaptation."
— 👩💻 DynamicWebPaige (@DynamicWebPaige) December 24, 2019
📄: https://t.co/JVVp7EHFhp
💻: https://t.co/e70ZSTHiZ9 pic.twitter.com/h3UpcG00od
Can you separately classify 3 constituent elements in a handwritten language? 🤔📝
— Kaggle (@kaggle) December 20, 2019
Join the https://t.co/y95NjMvlMM research competition today: https://t.co/vZ8bmOjR9Q #kagglecompetition pic.twitter.com/TM9nyr5YAX
The new Portrait Mode for Pixel 4 leverages dual cameras and the dual-pixel auto-focus system to improve depth estimation, allowing users to take professional-looking shallow depth of field images both close-up and at a distance. Learn how it was done at https://t.co/Z0S9REj5ES pic.twitter.com/mF1btwQt5O
— Google AI (@GoogleAI) December 16, 2019
.@tddammo pointed out to me that there's this really cool looking project on GitHub that uses attention in a cool way that strangely hasn't received much attention (3 stars). Lack of readme, perhaps. But check out the notebook: https://t.co/9vFDSRCOTu pic.twitter.com/hmhJY0iC7e
— Jason Antic (@citnaj) December 14, 2019
church of StyleGAN pic.twitter.com/yWbir1AoMG
— Gene Kogan (@genekogan) December 13, 2019
Deepfake Detection Challenge on #Kaggle: Identify videos with facial manipulations
— Alexandr Kalinin (@alxndrkalinin) December 11, 2019
- $1,000,000 USD in prize money
- Kaggle notebook-only submission
- over 470GB of training data
- 1GB limit on external data
- no custom packages or internet accesshttps://t.co/BUU9NFqyaz pic.twitter.com/FpBVvcu9kG
OpenCV Vehicle Detection, Tracking, and Speed Estimation via @pyimagesearch https://t.co/klkCIWT38K #python #opencv #iot #raspberrypi pic.twitter.com/kMTBz8qZnM
— Python Weekly (@PythonWeekly) December 11, 2019
Very cyberpunk: "We instead ... perform pixel-level image translation via CycleGAN to convert the [video of a human demonstrating a task] into a video of a robot, which can then be used to construct a reward function for a model-based RL algorithm." https://t.co/NcoMeFeNkZ
— Miles Brundage (@Miles_Brundage) December 11, 2019
If you're looking for a dataset to quickly try out your ideas for semi-supervised and unbalanced data classification, you might be interested in this new dataset: "Image网" (pronounced "Imagewang").https://t.co/Xmmz4cd9jI pic.twitter.com/mcaD3BqJuI
— Jeremy Howard (@jeremyphoward) December 11, 2019