NeuCLIP: Efficient Large-Scale CLIP Training with Neural Normalizer Optimization

On November 12, 2025, the article titled 'NeuCLIP: Efficient Large-Scale CLIP Training with Neural Normalizer Optimization' was submitted to arXiv, highlighting a significant advancement in training Contrastive Language-Image Pre-training (CLIP) models. The challenge of accurately estimating the normalization term in contrastive loss has long hindered effective training, particularly as conventional methods rely heavily on large batches, which demand substantial computational resources. NeuCLIP proposes a novel approach by reformulating the contrastive loss into a minimization problem and transforming it through variational analysis. This allows for more accurate normalizer estimates, addressing the optimization errors that arise when using smaller batches. The introduction of an alternating optimization algorithm enables the simultaneous training of the CLIP model and an auxiliary network, enhancing the overall efficiency of the training process. This development is crucial as it open…