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Self-supervised learning from images with a joint-embedding predictive architecture
This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …
Emerging properties in self-supervised vision transformers
In this paper, we question if self-supervised learning provides new properties to Vision
Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the …
Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the …
Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
This paper proposes a novel method of learning by predicting view assignments with
support samples (PAWS). The method trains a model to minimize a consistency loss, which …
support samples (PAWS). The method trains a model to minimize a consistency loss, which …
Semi-supervised learning made simple with self-supervised clustering
Self-supervised learning models have been shown to learn rich visual representations
without requiring human annotations. However, in many real-world scenarios, labels are …
without requiring human annotations. However, in many real-world scenarios, labels are …
The hidden uniform cluster prior in self-supervised learning
M Assran, R Balestriero, Q Duval, F Bordes… - ar** contrastive learning
Contrastive learning has shown outstanding performances in both supervised and
unsupervised learning, and has recently been introduced to solve weakly supervised …
unsupervised learning, and has recently been introduced to solve weakly supervised …