Self-Supervised Learning: Self-Prediction and Contrastive Learning | Tutorial | NeurIPS 2021
Email at: khawar512@gmail.com
In the world of artificial intelligence, self-supervised learning is a game-changing technique for training models using unlabelled data. Self-prediction and contrastive learning are two popular methods of self-supervised learning that have been successful in various applications, such as image and speech recognition.
In this video, we will dive deep into the concepts of self-supervised learning, self-prediction, and contrastive learning. We will explain how these techniques work, and explore their advantages over traditional supervised learning methods.
You will learn about the key components of self-supervised learning, such as pretext tasks and feature extraction, and see how they enable models to learn from unlabelled data. We will also provide examples of real-world applications of self-supervised learning, including the popular BERT model for natural language processing.
So, whether you are a beginner in AI or an experienced practitioner, this video will provide you with valuable insights into the world of self-supervised learning.
Keywords:
Self-supervised learning, self-prediction, contrastive learning, unsupervised learning, pretext tasks, feature extraction, BERT model, natural language processing, artificial intelligence, machine learning.
Видео Self-Supervised Learning: Self-Prediction and Contrastive Learning | Tutorial | NeurIPS 2021 канала Artificial Intelligence
In the world of artificial intelligence, self-supervised learning is a game-changing technique for training models using unlabelled data. Self-prediction and contrastive learning are two popular methods of self-supervised learning that have been successful in various applications, such as image and speech recognition.
In this video, we will dive deep into the concepts of self-supervised learning, self-prediction, and contrastive learning. We will explain how these techniques work, and explore their advantages over traditional supervised learning methods.
You will learn about the key components of self-supervised learning, such as pretext tasks and feature extraction, and see how they enable models to learn from unlabelled data. We will also provide examples of real-world applications of self-supervised learning, including the popular BERT model for natural language processing.
So, whether you are a beginner in AI or an experienced practitioner, this video will provide you with valuable insights into the world of self-supervised learning.
Keywords:
Self-supervised learning, self-prediction, contrastive learning, unsupervised learning, pretext tasks, feature extraction, BERT model, natural language processing, artificial intelligence, machine learning.
Видео Self-Supervised Learning: Self-Prediction and Contrastive Learning | Tutorial | NeurIPS 2021 канала Artificial Intelligence
Показать
Комментарии отсутствуют
Информация о видео
25 декабря 2021 г. 14:59:41
02:27:50
Другие видео канала
![Adversarial Parametric Pose Prior | CVPR 2022](https://i.ytimg.com/vi/3-HR9XHYKpM/default.jpg)
![Vector Quantized Diffusion Model for Text to Image Synthesis | CVPR 2022](https://i.ytimg.com/vi/H-etJzT6Jd8/default.jpg)
![Rethinking Semantic Segmentation: A Prototype View | CVPR 2022](https://i.ytimg.com/vi/Jm2wKObfES0/default.jpg)
![A ViT: Adaptive Tokens for Efficient Vision Transformer | CVPR 2022](https://i.ytimg.com/vi/dQwMLi2lf_g/default.jpg)
![3 MathildeCaron](https://i.ytimg.com/vi/SP7zbHJRh7w/default.jpg)
![Disentangling Visual and Written Concepts in CLIP | CVPR 2022](https://i.ytimg.com/vi/9P18KPOc-HY/default.jpg)
![RegNeRF: Regularizing Neural Radiance Fields for View Synthesis From Sparse Inputs | CVPR 2022](https://i.ytimg.com/vi/c2mN92BU7zU/default.jpg)
![Pointly Supervised Instance Segmentation | CVPR 2022](https://i.ytimg.com/vi/HoHWg8lWjKE/default.jpg)
![General Facial Representation Learning in a Visual Linguistic Manner | CVPR 2022](https://i.ytimg.com/vi/Z7VgD5pCfFY/default.jpg)
![Revealing Occlusions With 4D Neural Fields | CVPR 2022](https://i.ytimg.com/vi/L5pOsZ0BY4s/default.jpg)
![Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification | CVPR 2022](https://i.ytimg.com/vi/eb4r289Z0d4/default.jpg)
![Burst Image Restoration and Enhancement | CVPR 2022](https://i.ytimg.com/vi/GosaU2x6QCo/default.jpg)
![Balanced Multimodal Learning via On the Fly Gradient Modulation | CVPR 2022](https://i.ytimg.com/vi/k0NvQYl26B8/default.jpg)
![Framing RNN as a kernel method: A neural ODE approach | Oral Paper | NeurIPS 2021](https://i.ytimg.com/vi/2_MF2LX9Q5E/default.jpg)
![Efficient Training of Retrieval Models using Negative Cache | NeurIPS 2021](https://i.ytimg.com/vi/FncbQ2HGoEA/default.jpg)
![Super Fibonacci Spirals: Fast, Low Discrepancy Sampling of SO3 | CVPR 2022](https://i.ytimg.com/vi/uE8BMnudb5I/default.jpg)
![SelfRecon: Self Reconstruction Your Digital Avatar From Monocular Video | CVPR 2022](https://i.ytimg.com/vi/xYvQEW4ezU4/default.jpg)
![DF GAN: A Simple and Effective Baseline for Text to Image Synthesis | CVPR 2022](https://i.ytimg.com/vi/8TIq7JJHLlc/default.jpg)
![Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object | CVPR 2022](https://i.ytimg.com/vi/D9YjoJnj_M4/default.jpg)
![Deformable Sprites for Unsupervised Video Decomposition | CVPR 2022](https://i.ytimg.com/vi/BHY7jS3dFqk/default.jpg)
![LiDAR Snowfall Simulation for Robust 3D Object Detection | CVPR 2022](https://i.ytimg.com/vi/4PUbiSwhMYI/default.jpg)