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In addition, Albumentations has been used in many winning solutions for computer vision competitions, including the DeepFake Detection challenge at Kaggle with a prize of 1 million dollars. [ 6 ] Example
In order to assess the most effective algorithms for detecting deepfakes, a coalition of leading technology companies hosted the Deepfake Detection Challenge to accelerate the technology for identifying manipulated content. [174] The winning model of the Deepfake Detection Challenge was 65% accurate on the holdout set of 4,000 videos. [175]
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.
There are two markups for Outlier detection (point anomalies) and Changepoint detection (collective anomalies) problems 30+ files (v0.9) CSV Anomaly detection: 2020 (continually updated) [329] [330] Iurii D. Katser and Vyacheslav O. Kozitsin On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
Another recent challenge is the ADD [77] —Audio Deepfake Detection—which considers fake situations in a more real-life scenario. [ 78 ] Also the Voice Conversion Challenge [ 79 ] is a bi-annual challenge, created with the need to compare different voice conversion systems and approaches using the same voice data.