Sep 20, 2024 · In contrastive learning, your model learns to group “similar” images together and separate “dissimilar” ones by comparing their latent features (the stuff that lies underneath the …

Jul 24, 2021 · The success of constrastive learning in single-label classifications motivates us to leverage this learning framework to enhance distinctiveness for better performance in multi …

Mar 10, 2025 · In this paper, we propose a new contrastive learning method called Feature-Level Contrastive Learning (FLCL). FLCL conducts contrastive learning at the feature level and …

Feb 9, 2023 · In this paper, we propose a new image classification network model LCL by combining supervised learning and contrastive learning. The supervised learning component …

Nov 30, 2020 · Supervised Contrastive Learning (Prannay Khosla et al.) is a training methodology that outperforms supervised training with crossentropy on classification tasks. Essentially, …

Aug 1, 2023 · With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets. 1. …

Specifically, we propose a novel hybrid network structure be-ing composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, …

Implementation of self-supervised image-level contrastive pretraining methods using Keras.

5 days ago · Supervised contrastive learning. Advances in neural information processing systems 33 (2020), 18661–18673. Google Scholar [19] Yann LeCun, Léon Bottou, Yoshua Bengio, and …

Jul 5, 2021 · Experimental results show that compared with the naive self-supervised baseline, which learns tasks one by one without taking any technique, we improve the image …