keyword
multiview learning
Multiview learning is a machine learning paradigm in which models are trained using multiple distinct perspectives, feature subsets, or representations of the same underlying data samples. Unlike traditional single-view methods that rely on an isolated set of features, multiview approaches analyze diverse data sources—such as different sensory modalities, distinct feature descriptors, or augmented variations—to capture complementary information and uncover shared, view-invariant semantic properties. By integrating these views through strategies such as co-training, subspace alignment, or contrastive mutual information optimization, multiview learning enhances model generalization, improves robustness against noise or missing attributes, and yields richer representations for downstream classification, clustering, and representation learning tasks.
2 items
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
Why you should read this
Demonstrates through theory and experiments that reducing the mutual information between data views while retaining task-relevant features optimizes contrastive learning representations, setting a new benchmark for unsupervised ImageNet pre-training.
Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use theoretical and empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by-product, we achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification ( top-1 linear readout with a ResNet-50). In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre-training. Code:this http URL
Added
2026-09-25
License
Published with permission
Contrastive Multiview Coding
Yonglong Tian, Dilip Krishnan, Phillip Isola
Why you should read this
Introduces Contrastive Multiview Coding, a scalable self-supervised framework that maximizes mutual information across multiple sensory views, demonstrating that contrastive objectives outperform predictive reconstruction and that representation quality consistently improves as more views are integrated.
Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a “dog” can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks. Code is released at: http://github.com/HobbitLong/CMC/.
Added
2026-09-14