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Self-supervised Pretraining of Visual Features in the Wild
Priya Goyal,Mathilde Caron,Benjamin Lefaudeux,Min Xu,Pengchao Wang,Vivek S. Pai,Mannat Singh,Vitaliy Liptchinsky,Ishan Misra,Armand Joulin,Piotr Bojanowski +10 more
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Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods as mentioned in this paper. But self-learning cannot learn from any random image and from any unbounded dataset.Abstract:
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1% and confirming that self-supervised learning works in a real world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving 77.9% top-1 with access to only 10% of ImageNet. Code: this https URLread more
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Journal ArticleDOI
Self-supervised Learning: Generative or Contrastive.
TL;DR: This survey takes a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning, and comprehensively review the existing empirical methods into three main categories according to their objectives.
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Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron,Hugo Touvron,Hugo Touvron,Ishan Misra,Hervé Jégou,Julien Mairal,Piotr Bojanowski,Armand Joulin +7 more
TL;DR: In this paper, self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets) beyond the fact that adapting selfsupervised methods to this architecture works particularly well, they make the following observations: first, self-vised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets.
Journal ArticleDOI
Artificial intelligence and machine learning for medical imaging: A technology review.
Ana M. Barragan-Montero,Umair Javaid,Gilmer Valdes,Dan Nguyen,Paul Desbordes,Benoît Macq,S. Willems,Liesbeth Vandewinckele,Mats Holmström,Fredrik Löfman,Steven Michiels,Kevin Souris,Edmond Sterpin,John Aldo Lee +13 more
TL;DR: Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing as discussed by the authors.
Journal ArticleDOI
Review on self-supervised image recognition using deep neural networks
Kriti Ohri,Mukesh Kumar +1 more
TL;DR: Self-supervised learning as discussed by the authors is a form of unsupervised deep learning that allows the network to learn rich visual features that help in performing downstream computer vision tasks such as image classification, object detection, and image segmentation.
Posted ContentDOI
Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization
TL;DR: In this article, a deep learning-based approach for fully self-supervised protein localization profiling and clustering is presented, which does not require pre-existing knowledge, categories, or annotations.
References
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel,Noam Shazeer,Adam Roberts,Katherine Lee,Sharan Narang,Michael Matena,Yanqi Zhou,Wei Li,Peter J. Liu +8 more
TL;DR: This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.
Proceedings ArticleDOI
Extracting and composing robust features with denoising autoencoders
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan,Quoc V. Le +1 more
TL;DR: A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet.
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Representation Learning with Contrastive Predictive Coding
TL;DR: This work proposes a universal unsupervised learning approach to extract useful representations from high-dimensional data, which it calls Contrastive Predictive Coding, and demonstrates that the approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.
Proceedings ArticleDOI
Dimensionality Reduction by Learning an Invariant Mapping
TL;DR: This work presents a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold.