Search Results for author:
Found 9 papers, 1 papers with code
Multi-scale Unified Network for Image Classification
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition.
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
On the other hand, Semi-IPC learns a prototype for each class with unsupervised regularization, enabling the model to incrementally learn from partially labeled new data while maintaining the knowledge of old classes.
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data.
Federated Class-Incremental Learning with Prototype Guided Transformer
On the other hand, our approach utilizes a pre-trained model as the backbone and utilizes LoRA to fine-tune with a tiny amount of parameters when learning new classes.
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning
This is hard for DNN because it tends to focus on fitting to new classes while ignoring old classes, a phenomenon known as catastrophic forgetting.
Meta-Learning for Airflow Simulations with Graph Neural Networks
To this end, we present a meta-learning approach to enhance the performance of learned models on OoD samples.
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment.
Multi-modal Fusion Technology based on Vehicle Information: A Survey
Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years.
Deep Statistical Solvers
This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e. g., from system simulations.
