283 citations · 814 across the 69 of their papers we have counts for
16 papers · 1 filter
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
Lan Li, Bowen Tao, Lu Han +2
Differing from traditional semi-supervised learning, class-imbalanced semi-supervised learning presents two distinct challenges: (1) The imbalanced distribution of training samples…
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…
Learning Robust Precipitation Forecaster by Temporal Frame Interpolation
Lu Han, Xu-Yang Chen, Han-Jia Ye +1
Recent advances in deep learning have significantly elevated weather prediction models. However, these models often falter in real-world scenarios due to their sensitivity to spati…
Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective
Han-Jia Ye, Qi-Le Zhou, Huai-Hong Yin +2
Pre-training is prevalent in deep learning for vision and text data, leveraging knowledge from other datasets to enhance downstream tasks. However, for tabular data, the inherent h…
Unlocking the Transferability of Tokens in Deep Models for Tabular Data
Qi-Le Zhou, Han-Jia Ye, Le-Ye Wang +1
Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks. However, such a paradigm becomes particularly challenging with tab…
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…
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
Yi-Kai Zhang, Lu Ren, Chao Yi +3
The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning. Leveraging pre-trained…
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…
Model Spider: Learning to Rank Pre-Trained Models Efficiently
Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding +2
Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is essential to take advantage of plentiful model resources. With the availability of numerous hete…
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising
Fu-Yun Wang, Wenshuo Chen, Guanglu Song +3
Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and edi…
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…
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting
Lu Han, Han-Jia Ye, De-Chuan Zhan
Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predi…
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…
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
Lu Han, Han-Jia Ye, De-Chuan Zhan
When there are unlabeled Out-Of-Distribution (OOD) data from other classes, Semi-Supervised Learning (SSL) methods suffer from severe performance degradation and even get worse tha…