283 citations · 546 across the 36 of their papers we have counts for
7 papers · 1 filter
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang +2
Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new class…
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
Hai-Long Sun, Da-Wei Zhou, De-Chuan Zhan +1
While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing wit…
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
Qi-Wei Wang, Hongyu Lu, Yu Chen +4
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. S…
Learning without Forgetting for Vision-Language Models
Da-Wei Zhou, Yuanhan Zhang, Yan Wang +4
Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former o…
Preserving Locality in Vision Transformers for Class Incremental Learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye +1
Learning new classes without forgetting is crucial for real-world applications for a classification model. Vision Transformers (ViT) recently achieve remarkable performance in Clas…
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye +2
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge…
Class-Incremental Learning: A Survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi +3
Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in…