The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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:2608. 11661v1 Announce Type: cross Abstract: A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings.
arXiv:2606. 26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute.
The paper investigates the expressive power of multimodal contrastive learning architectures by treating them as parameterized families of joint density estimators. It shows that the classic two‑tower CLIP model is a universal approximator for two modalities, while a common extension that sums pairwise similarities fails to approximate arbitrary joint distributions when three or more modalities are involved, though it can match all pairwise conditionals. To address this limitation, the authors introduce Hadamard‑CLIP, which adds a single learned weight vector to restore universal approximation for any number of modalities while retaining CLIP’s efficient retrieval capabilities.
arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.
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.