research
∙
09/13/2023
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
While traditional machine learning can effectively tackle a wide range o...
share
research
∙
08/17/2023
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
The rapid expansion of foundation pre-trained models and their fine-tune...
research
∙
07/14/2023
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
The Click-Through Rate (CTR) prediction task is critical in industrial r...
research
∙
06/06/2023
Model Spider: Learning to Rank Pre-Trained Models Efficiently
Figuring out which Pre-Trained Model (PTM) from a model zoo fits the tar...
research
∙
05/30/2023
Learning without Forgetting for Vision-Language Models
Class-Incremental Learning (CIL) or continual learning is a desired capa...
research
∙
04/14/2023
Preserving Locality in Vision Transformers for Class Incremental Learning
Learning new classes without forgetting is crucial for real-world applic...
research
∙
04/11/2023
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting
Multivariate time series data comprises various channels of variables. T...
research
∙
03/13/2023
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Class-incremental learning (CIL) aims to adapt to emerging new classes w...
research
∙
02/07/2023
Deep Class-Incremental Learning: A Survey
Deep models, e.g., CNNs and Vision Transformers, have achieved impressiv...
research
∙
01/15/2023
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
When there are unlabeled Out-Of-Distribution (OOD) data from other class...
research
∙
06/01/2022
Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap
Traditional self-supervised contrastive learning methods learn embedding...
research
∙
05/26/2022
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
Real-world applications require the classification model to adapt to new...
research
∙
05/04/2022
Generalized Knowledge Distillation via Relationship Matching
The knowledge of a well-trained deep neural network (a.k.a. the "teacher...
research
∙
04/25/2022
Faculty Distillation with Optimal Transport
Knowledge distillation (KD) has shown its effectiveness in improving a s...
research
∙
04/10/2022
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
The ability to learn new concepts continually is necessary in this ever-...
research
∙
04/08/2022
Identifying Ambiguous Similarity Conditions via Semantic Matching
Rich semantics inside an image result in its ambiguous relationship with...
research
∙
03/31/2022
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
New classes arise frequently in our ever-changing world, e.g., emerging ...
research
∙
03/14/2022
Forward Compatible Few-Shot Class-Incremental Learning
Novel classes frequently arise in our dynamically changing world, e.g., ...
research
∙
12/23/2021
PyCIL: A Python Toolbox for Class-Incremental Learning
Traditional machine learning systems are deployed under the closed-world...
research
∙
07/27/2021
Co-Transport for Class-Incremental Learning
Traditional learning systems are trained in closed-world for a fixed num...
research
∙
07/01/2021
Few-Shot Learning with a Strong Teacher
Few-shot learning (FSL) aims to train a strong classifier using limited ...
research
∙
06/30/2021
How to Train Your MAML to Excel in Few-Shot Classification
Model-agnostic meta-learning (MAML) is arguably the most popular meta-le...
research
∙
06/15/2021
Contextualizing Multiple Tasks via Learning to Decompose
One single instance could possess multiple portraits and reveal diverse ...
research
∙
04/08/2021
Few-Shot Action Recognition with Compromised Metric via Optimal Transport
Although vital to computer vision systems, few-shot action recognition i...
research
∙
04/08/2021
Support-Target Protocol for Meta-Learning
The support/query (S/Q) training protocol is widely used in meta-learnin...
research
∙
04/05/2021
Procrustean Training for Imbalanced Deep Learning
Neural networks trained with class-imbalanced data are known to perform ...
research
∙
03/28/2021
Learning Placeholders for Open-Set Recognition
Traditional classifiers are deployed under closed-set setting, with both...
research
∙
12/17/2020
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
While deep learning demonstrates its strong ability to handle independen...
research
∙
11/30/2020
Revisiting Unsupervised Meta-Learning: Amplifying or Compensating for the Characteristics of Few-Shot Tasks
Meta-learning becomes a practical approach towards few-shot image classi...
research
∙
03/01/2020
Novelty-Prepared Few-Shot Classification
Few-shot classification algorithms can alleviate the data scarceness iss...
research
∙
02/03/2020
Revisiting Meta-Learning as Supervised Learning
Recent years have witnessed an abundance of new publications and approac...
research
∙
01/06/2020
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
We investigate learning a ConvNet classifier with class-imbalanced data....
research
∙
06/07/2019
Learning Classifier Synthesis for Generalized Few-Shot Learning
Visual recognition in real-world requires handling long-tailed and even ...
research
∙
12/10/2018