SSCLNet: A Self-Supervised Contrastive Loss-Based Pre-Trained Network for Brain MRI Classification
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3237542
Abstract: Brain magnetic resonance images (MRI) convey vital information for making diagnostic decisions and are widely used to detect brain tumors. This research proposes a self-supervised pre-training method based on feature representation learning through contrastive loss… read more here.
Keywords: brain; contrastive loss; loss based; self supervised ... See more keywords
Batch Hard Contrastive Loss and Its Application to Cross-View Gait Recognition
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3262271
Abstract: Biometric person authentication comprises two tasks: the identification task (i.e., one-to-many matching) and the verification task (i.e., one-to-one matching). In this paper, we propose a loss function called batch hard contrastive loss (BHCn) for the… read more here.
Keywords: view; batch hard; loss; contrastive loss ... See more keywords
Multiagent Path Finding Using Deep Reinforcement Learning Coupled With Hot Supervision Contrastive Loss
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2022.3206745
Abstract: Multiagent path finding (MAPF) is employed to find collision-free paths to guide agents traveling from an initial to a target position. The advanced decentralized approach utilizes communication between agents to improve their performance in environments… read more here.
Keywords: reinforcement learning; path finding; contrastive loss; multiagent path ... See more keywords
Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2022.3161681
Abstract: The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For… read more here.
Keywords: retinal vessel; contrastive loss; vessel segmentation;
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets
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DOI: 10.3390/e24091303
Abstract: Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space. The learning process is typically conducted… read more here.
Keywords: imbalanced datasets; handling imbalanced; loss; contrastive loss ... See more keywords