Mar 2, 2021 · Abstract:Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods.
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This work explores if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision, and observes that ...
Mar 26, 2021 · Self-supervised learning is expected to learn from any random image and from any unbounded dataset. SElf-supERvised (SEER) is proposed, a ...
Aug 28, 2023 · SEER Self-supervised features transfer better than supervised features regardless of the pretraining data. Downstream Detection and Segmentation.
Self-supervised Pretraining of Visual Features in the Wild - ar5iv - arXiv
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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- ...
SEER is a self-supervised learning approach for training large models on random, uncurated images with no supervision.
Mar 29, 2021 · For both downstream tasks and architectures, our self-supervised pre-training outperforms supervised pretraining by 1.5 − 2 AP points. However, ...
This model, called SEER (SElf-supERvised), could then offer significant improvements for downstream tasks such as image classification, segmentation, or object ...
In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image ...
Self-supervised learning (SSL) is an approach to pretrain models with unlabeled datasets and extract useful feature representations such that these models ...