Results 61 to 70 of about 12,484 (262)

TO-UGDA: target-oriented unsupervised graph domain adaptation

Scientific Reports
Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in ...
Zhuo Zeng, Jianyu Xie, Zhijie Yang, Tengfei Ma, Duanbing Chen   +4 more
doaj   +1 more source

Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing

IEEE Access
In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. However, these deep models have poor generalization ability across different domain datasets.
Xiaojing Li, Wei Zhou, Mingjian Jiang
doaj   +1 more source

Triplet Loss Network for Unsupervised Domain Adaptation

Algorithms, 2019
Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain.
Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan, Asad Masood Khattak   +4 more
doaj   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures

Advanced Robotics Research, EarlyView.
This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley   +1 more source

Gradient Harmonization in Unsupervised Domain Adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE TPAMI ...
Fuxiang Huang, Suqi Song, Lei Zhang
openaire   +3 more sources

Temporal and Cell‐Specific Regulation of Synaptic Homeostasis by the Chromatin Remodeler Chd1

Advanced Science, EarlyView.
Chd1, the Drosophila homologue of mammalian CHD2 ‐ a gene linked to autism, epilepsy, and intellectual disability, is required for synaptic homeostatic plasticity. Chd1 in glia is necessary for the rapid induction of synaptic homeostasis, whereas Chd1 in motoneurons, muscle, and glia is critical for long‐term maintenance.
Danielle T. Morency, Tao Cui, Yimei Cai, Chloe Lok, Rachel E. Nokku, Ruoxian Huang, Grace L. Chu, Yumeng Xie, Saleem W. Abu‐Tayeh, Kaikai He, Chengjie Qiu, Junyi Wang, Paxton M. Paganelli, Ting Wang, Gabrielle Williams, Sreejith Nair, Huadong Pei, Dion K. Dickman, Stefano Vicini, Tingting Wang   +19 more
wiley   +1 more source

Transferable adversarial masked self-distillation for unsupervised domain adaptation

Complex & Intelligent Systems, 2023
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN ...
Yuelong Xia, Li-Jun Yun, Chengfu Yang
doaj   +1 more source