Embedding Geometries of Contrastive Language-Image Pre-Training

Fuente: arXiv
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Main Authors: Chou, Jason Chuan-Chih, Alam, Nahid
Format: Preprint
Published: 2024
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author Chou, Jason Chuan-Chih
Alam, Nahid
author_facet Chou, Jason Chuan-Chih
Alam, Nahid
contents Since the publication of CLIP, the approach of using InfoNCE loss for contrastive pre-training has become widely popular for bridging two or more modalities. Despite its wide adoption, CLIP's original design choices of L2 normalization and cosine similarity logit have rarely been revisited. We have systematically experimented with alternative geometries and softmax logits for language-image pre-training and identified that variants with intuitive Euclidean geometry, Euclidean CLIP (EuCLIP), match or exceed the performance of CLIP and support hierarchical relationships at least as well as more complicated hyperbolic alternative.
format Preprint
id arxiv_https___arxiv_org_abs_2409_13079
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Embedding Geometries of Contrastive Language-Image Pre-Training
Chou, Jason Chuan-Chih
Alam, Nahid
Machine Learning
Computation and Language
Computer Vision and Pattern Recognition
Since the publication of CLIP, the approach of using InfoNCE loss for contrastive pre-training has become widely popular for bridging two or more modalities. Despite its wide adoption, CLIP's original design choices of L2 normalization and cosine similarity logit have rarely been revisited. We have systematically experimented with alternative geometries and softmax logits for language-image pre-training and identified that variants with intuitive Euclidean geometry, Euclidean CLIP (EuCLIP), match or exceed the performance of CLIP and support hierarchical relationships at least as well as more complicated hyperbolic alternative.
title Embedding Geometries of Contrastive Language-Image Pre-Training
topic Machine Learning
Computation and Language
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2409.13079