Search Results for author:
Found 6 papers, 5 papers with code
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems.
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning.
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
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.
Deep Class-Incremental Learning: A Survey
Deep models, e. g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world.
Learning Debiased Representations via Conditional Attribute Interpolation
When a dataset is biased, i. e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn.
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets.
