Self-supervised QA from Facebook AI
The researchers from Facebook AI published a paper with the results of exploring the idea of unsupervised extractive question answering and the following training of the supervised question answering model. This approach achieves 56.41F1 on SQuAD2 dataset.
Original paper: https://research.fb.com/wp-content/uploads/2019/07/Unsupervised-Question-Answering-by-Cloze-Translation.pdf?
Code for experiments: https://github.com/facebookresearch/UnsupervisedQA
#NLP #BERT #FacebookAI #SelfSupervised
The researchers from Facebook AI published a paper with the results of exploring the idea of unsupervised extractive question answering and the following training of the supervised question answering model. This approach achieves 56.41F1 on SQuAD2 dataset.
Original paper: https://research.fb.com/wp-content/uploads/2019/07/Unsupervised-Question-Answering-by-Cloze-Translation.pdf?
Code for experiments: https://github.com/facebookresearch/UnsupervisedQA
#NLP #BERT #FacebookAI #SelfSupervised
โโself-supervised learning
the recent time more & more talk about self-supervised learning; maybe because each year increase data, how to know
the authors (lilian weng @ openai) cover the main ideas in this area on
โข images (distortion, patches, colorization, generative modeling, contrastive predictive coding, momentum contrast)
โข videos (tracking, frame sequence, video colorization)
โข control problems (multi-view metric learning, autonomous goal generation)
btw, this article is being updated
article: https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html
#selfsupervised #learning #pretext #unlabel
the recent time more & more talk about self-supervised learning; maybe because each year increase data, how to know
the authors (lilian weng @ openai) cover the main ideas in this area on
โข images (distortion, patches, colorization, generative modeling, contrastive predictive coding, momentum contrast)
โข videos (tracking, frame sequence, video colorization)
โข control problems (multi-view metric learning, autonomous goal generation)
btw, this article is being updated
article: https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html
#selfsupervised #learning #pretext #unlabel
โโJigsawGAN: Self-supervised Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks
The authors suggest a GAN-based approach for solving jigsaw puzzles. JigsawGAN is a self-supervised method with a multi-task pipeline: classification branch classifies jigsaw permutations, GAN branch recovers features to images with the correct order.
The proposed method can solve jigsaw puzzles efficiently by utilizing both semantic information and edge information simultaneously.
Paper: https://arxiv.org/abs/2101.07555
#deeplearning #jigsaw #selfsupervised #gan
The authors suggest a GAN-based approach for solving jigsaw puzzles. JigsawGAN is a self-supervised method with a multi-task pipeline: classification branch classifies jigsaw permutations, GAN branch recovers features to images with the correct order.
The proposed method can solve jigsaw puzzles efficiently by utilizing both semantic information and edge information simultaneously.
Paper: https://arxiv.org/abs/2101.07555
#deeplearning #jigsaw #selfsupervised #gan
SEER: The start of a more powerful, flexible, and accessible era for computer vision
#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.
And here it is โ model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.
Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)
Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf
#facebook #fair #cv #dl
#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.
And here it is โ model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.
Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)
Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf
#facebook #fair #cv #dl
Meta
SEER: The start of a more powerful, flexible, and accessible era for computer vision
The future of AI is in creating systems that can learn directly from whatever information theyโre given โ whether itโs text, images, or another type of data โ without relying on carefully curated and labeled data sets to teach them how to recognize objectsโฆ
โโBiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
Introducing the groundbreaking Biomedical Generative Pre-trained Transformer (BiomedGPT) model, this paper revolutionizes the field of biomedicine by offering a unified and generalist approach. BiomedGPT harnesses the power of self-supervision on extensive and diverse datasets, enabling it to effortlessly handle multi-modal inputs and excel in a wide range of downstream tasks. In a series of comprehensive experiments, BiomedGPT astoundingly outperforms its predecessors, emerging as the unrivaled leader across five distinct tasks and a staggering 20 public datasets encompassing over 15 unique biomedical modalities. Its ability to deliver expansive and all-encompassing representations of biomedical data heralds a significant advancement in the field, with promising implications for improving healthcare outcomes.
Through meticulous ablation studies, the efficacy of BiomedGPT's multi-modal and multi-task pretraining approach is vividly showcased. This groundbreaking model effortlessly transfers its vast knowledge to previously unseen data, demonstrating its versatility and adaptability. The implications of this research are profound, paving the way for the development of unified and all-encompassing models for biomedicine.
Paper link: https://arxiv.org/abs/2305.17100
Code link: https://github.com/taokz/BiomedGPT
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-biomedgpt
#deeplearning #nlp #selfsupervised #gpt #biomedicine
Introducing the groundbreaking Biomedical Generative Pre-trained Transformer (BiomedGPT) model, this paper revolutionizes the field of biomedicine by offering a unified and generalist approach. BiomedGPT harnesses the power of self-supervision on extensive and diverse datasets, enabling it to effortlessly handle multi-modal inputs and excel in a wide range of downstream tasks. In a series of comprehensive experiments, BiomedGPT astoundingly outperforms its predecessors, emerging as the unrivaled leader across five distinct tasks and a staggering 20 public datasets encompassing over 15 unique biomedical modalities. Its ability to deliver expansive and all-encompassing representations of biomedical data heralds a significant advancement in the field, with promising implications for improving healthcare outcomes.
Through meticulous ablation studies, the efficacy of BiomedGPT's multi-modal and multi-task pretraining approach is vividly showcased. This groundbreaking model effortlessly transfers its vast knowledge to previously unseen data, demonstrating its versatility and adaptability. The implications of this research are profound, paving the way for the development of unified and all-encompassing models for biomedicine.
Paper link: https://arxiv.org/abs/2305.17100
Code link: https://github.com/taokz/BiomedGPT
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-biomedgpt
#deeplearning #nlp #selfsupervised #gpt #biomedicine
