Results on CIFAR-10 & CIFAR-100.

Results on CIFAR-10 & CIFAR-100.

<div><p>We introduce PiCCL (Primary Component Contrastive Learning), a self-supervised contrastive learning framework that utilizes a multiplex Siamese network structure consisting of many identical branches rather than 2 to maximize learning efficiency. PiCCL is simple and light weight,...

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Bibliographic Details
Main Author: Yiming Kuang (22120458) (author)
Other Authors: Jianwu Guan (22120461) (author), Hongyun Liu (185898) (author), Fei Chen (13690) (author), Zihua Wang (290751) (author), Weidong Wang (321040) (author)
Published: 2025
Subjects:
Science Policy
Space Science
Biological Sciences not elsewhere classified
use asymmetric networks
maximize learning efficiency
intricate pretext tasks
image numerous times
image augmentation paradigm
could hinder performance
3 datasets respectively
primary component loss
compute loss functions
piccl &# 8217
network loss
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various state
unique structure
take advantage
supervised algorithms
piccl excels
multimodal data
light weight
introduce piccl
computationally lightweight
calculated using
benchmarked piccl
batch size
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