Results 51 to 60 of about 14,201 (266)

CC-GNN: A Clustering Contrastive Learning Network for Graph Semi-Supervised Learning

IEEE Access
In graph modeling, scarcity of labeled data is a challenging issue. To address this issue, state-of-the-art graph models learn the representation of graph data via contrastive learning.
Peng Qin, Weifu Chen, Min Zhang, Defang Li, Guocan Feng   +4 more
doaj   +1 more source

Robust age estimation model using group‐aware contrastive learning

IET Image Processing, 2022
Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments.
Xiaoqiang Li, Chengyu Guo, Yifan Wu, Congcong Zhu, Jide Li   +4 more
doaj   +1 more source

Spectral Temporal Contrastive Learning

CoRR, 2023
Accepted to Self-Supervised Learning - Theory and Practice, NeurIPS Workshop ...
Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou, Guy Wolf   +3 more
openaire   +2 more sources

Conditional Contrastive Learning with Kernel

CoRR, 2022
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the ...
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao 0002, Kun Zhang 0001, Louis-Philippe Morency, Ruslan Salakhutdinov   +6 more
openaire   +3 more sources

CoRF-Yoga: Cross-Modal Contrastive Learning for Robust, Feasible Yoga Pose Recognition

IEEE Access
Recognizing yoga poses in real-world environments is challenging due to sensor-dependent noise, execution variability, and the high cost of large-scale manual annotation, which limits the scalability of existing supervised approaches.
Zolboo Damiran, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa, Keiichi Yasumoto   +4 more
doaj   +1 more source

Guidelines for Pediatric Radiotherapy Simulation: A Report From the Children's Oncology Group Radiation Oncology Discipline

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Pediatric radiation therapy presents unique challenges compared to adult treatments, including those of immobilization, potential need for sedation, and the critical importance of accurate, reproducible positioning. Additionally, heightened attention to imaging doses is necessary to minimize long‐term toxicity in survivors.
Parham Alaei, Stephen Mihalcik, Sujith Baliga, Young Kwok, Dana Casey, Eric Ehler, Christiane Burton, John C. Breneman, Daphne A. Haas‐Kogan, Suzanne L. Wolden, Arnold C. Paulino, Nadia N. Laack, Natia Esiashvili, Sarah Milgrom, Christine Hill‐Kayser, Thomas J. Fitzgerald, John A. Kalapurakal, Chia‐Ho Hua   +17 more
wiley   +1 more source

Contrastive Mask Learning for Self-Supervised 3D Skeleton-Based Action Recognition

Sensors
In this paper, we propose a contrastive mask learning (CML) method for self-supervised 3D skeleton-based action recognition. Specifically, the mask modeling mechanism is integrated into multi-level contrastive learning with the aim of forming a mutually ...
Haoyuan Zhang
doaj   +1 more source

Early Impact of Childhood Opportunity on Neurocognitive Outcomes in Sickle Cell Disease

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Neurocognitive impairment is a well‐recognized complication of sickle cell disease (SCD) that begins early in childhood and persists across development. While cerebrovascular injury contributes substantially to risk, neurocognitive deficits are also observed in children without overt or silent cerebral infarctions, suggesting ...
Julia E. LaMotte, Hannah Koch, Olivia A. Coughlin, Jillian R. Bouck, Allie Carter, Seethal A. Jacob   +5 more
wiley   +1 more source

Tuned Contrastive Learning

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
In recent times, contrastive learning based loss functions have become increasingly popular for visual self-supervised representation learning owing to their state-of-the-art (SOTA) performance. Most of the modern contrastive learning methods generalize only to one positive and multiple negatives per anchor.
Chaitanya Animesh, Manmohan Chandraker
openaire   +2 more sources

Contrastive Learning for Inference in Dialogue

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Accepted to ...
Etsuko Ishii, Yan Xu 0012, Bryan Wilie, Ziwei Ji 0001, Holy Lovenia, Willy Chung, Pascale Fung   +6 more
openaire   +2 more sources