The paper introduces the concept of internal discrepancy, measuring the gap between the mean representation of positive samples and latent cluster centers in contrastive learning. It derives a generalization bound for downstream supervised tasks that depends on this discrepancy, providing a unifying framework for existing assumptions and motivating a relaxed k‑means loss to reduce it. The study also explores how contrastive learning affects downstream behavior, particularly in class‑imbalanced settings, and establishes properties of the resulting supervised losses.
arXiv:2606. 04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood.
arXiv:2602. 24012v2 Announce Type: replace Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) models.
arXiv:2609.33741v3 Announce Type: replace-cross Abstract: Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pai...
The paper investigates whether supervised contrastive learning objectives produce a regular simplex geometry in representation space when trained on long‑tailed data. It shows that existing objectives fail to achieve this geometry because they depend on class sample counts. The authors propose a fully balanced contrastive objective that provably forms a regular simplex, leading to a more balanced representation space and improved performance on long‑tailed recognition and transfer learning tasks.
arXiv:2606. 26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute.
arXiv:2607. 09405v1 Announce Type: new Abstract: Similarity search is a primary application of embedding models trained by contrastive learning.
arXiv:2606. 25232v1 Announce Type: new Abstract: Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones.
The paper introduces Inverted Contrastive Learning for Unsupervised Feature Selection (ICLFS), a method that treats each feature as a sample by inverting the data matrix and applies a contrastive learning framework to learn consistent representations across masked positive views and a shuffled negative view. Feature saliency is derived from the magnitude of projector‑space embeddings, and a Laplacian‑Gated Ranking Correction step refines the ranking by reducing local redundancy. Experiments on 12 benchmark datasets show that ICLFS achieves the best clustering accuracy on 10 datasets compared to both classical and neural baselines, demonstrating the effectiveness of feature‑wise contrastive consistency for unsupervised feature selection.
arXiv:2606. 30625v1 Announce Type: cross Abstract: Contrastive embedding models trained with scale-invariant losses are typically paired with distance metrics like cosine similarity, effectively ignoring embedding magnitudes.
arXiv:2607. 27660v1 Announce Type: new Abstract: Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and multimodal learning.
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
arXiv:2609.06460v1 Announce Type: new Abstract: Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various...