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Found 8 papers, 3 papers with code
SLP-Net:An efficient lightweight network for segmentation of skin lesions
Prompt treatment for melanoma is crucial.
Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples
Contrastive learning predicts whether two images belong to the same category by training a model to make their feature representations as close or as far away as possible.
MNN: Mixed Nearest-Neighbors for Self-Supervised Learning
In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples.
Multi-network Contrastive Learning Based on Global and Local Representations
We introduce global and local feature information for self-supervised contrastive learning through multiple networks.
MSVQ: Self-Supervised Learning with Multiple Sample Views and Queues
Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning.
Synthetic Hard Negative Samples for Contrastive Learning
Contrastive learning has emerged as an essential approach for self-supervised learning in visual representation learning.
Advancing Example Exploitation Can Alleviate Critical Challenges in Adversarial Training
However, they are susceptible to adversarial examples, which are generated by adding adversarial perturbations to original data.
Learning Task-aware Robust Deep Learning Systems
Many works demonstrate that deep learning system is vulnerable to adversarial attack.
