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Found 12 papers, 4 papers with code
Learning and Leveraging World Models in Visual Representation Learning
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model.
Revisiting Feature Prediction for Learning Visual Representations from Video
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision.
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos.
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.
No Free Lunch in Self Supervised Representation Learning
In this work, we explore this relationship, its impact on a domain other than natural images, and show that designing the transformations can be viewed as a form of supervision.
VICRegL: Self-Supervised Learning of Local Visual Features
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features.
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning
This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream.
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Even with 32x32 patch representation, BagSSL achieves 62% top-1 linear probing accuracy on ImageNet.
On the duality between contrastive and non-contrastive self-supervised learning
Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches.
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image.
Representation Learning
Self-Supervised Image Classification
+2
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks.
