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Found 6 papers, 3 papers with code
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations
To address these challenges, we propose an efficient and model-agnostic SCL framework named Supervised Sample-Label Contrastive Learning with Soft-HGR Maximal Correlation (SSLCL), which eliminates the need for a large batch size and can be seamlessly integrated with existing ERC models without introducing any model-specific assumptions.
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
The Spiking Neural Network (SNN) has attracted more and more attention recently.
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients.
Real Spike: Learning Real-valued Spikes for Spiking Neural Networks
Motivated by this assumption, a training-inference decoupling method for SNNs named as Real Spike is proposed, which not only enjoys both unshared convolution kernels and binary spikes in inference-time but also maintains both shared convolution kernels and Real-valued Spikes during training.
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence.
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning
Current transfer learning algorithm designs mainly focus on the similarities between source and target tasks, while the impacts of the sample sizes of these tasks are often not sufficiently addressed.
