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09/13/2023
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
While traditional machine learning can effectively tackle a wide range o...
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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...
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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...
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06/19/2023
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Model-based imitation learning (MBIL) is a popular reinforcement learnin...
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06/08/2023
COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features
With the development of the multi-media internet, visual characteristics...
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06/08/2023
Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping
Research has shown that deep networks tend to be overly optimistic about...
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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...
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05/30/2023
Learning without Forgetting for Vision-Language Models
Class-Incremental Learning (CIL) or continual learning is a desired capa...
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05/30/2023
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics
Aiding humans with scientific designs is one of the most exciting of art...
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05/07/2023
MrTF: Model Refinery for Transductive Federated Learning
We consider a real-world scenario in which a newly-established pilot pro...
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04/14/2023
Preserving Locality in Vision Transformers for Class Incremental Learning
Learning new classes without forgetting is crucial for real-world applic...
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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...
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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...
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02/07/2023
Deep Class-Incremental Learning: A Survey
Deep models, e.g., CNNs and Vision Transformers, have achieved impressiv...
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01/15/2023
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
When there are unlabeled Out-Of-Distribution (OOD) data from other class...
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01/05/2023
Self-Motivated Multi-Agent Exploration
In cooperative multi-agent reinforcement learning (CMARL), it is critica...
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06/17/2022
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Keyword spotting (KWS) aims to discriminate a specific wake-up word from...
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06/01/2022
Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap
Traditional self-supervised contrastive learning methods learn embedding...
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06/01/2022
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
Predicting conversion rate (e.g., the probability that a user will purch...
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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...
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05/04/2022
Generalized Knowledge Distillation via Relationship Matching
The knowledge of a well-trained deep neural network (a.k.a. the "teacher...
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04/25/2022
Faculty Distillation with Optimal Transport
Knowledge distillation (KD) has shown its effectiveness in improving a s...
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04/10/2022
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
The ability to learn new concepts continually is necessary in this ever-...
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04/08/2022
Identifying Ambiguous Similarity Conditions via Semantic Matching
Rich semantics inside an image result in its ambiguous relationship with...
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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 ...
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03/28/2022
Federated Learning with Position-Aware Neurons
Federated Learning (FL) fuses collaborative models from local nodes with...
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03/14/2022
Forward Compatible Few-Shot Class-Incremental Learning
Novel classes frequently arise in our dynamically changing world, e.g., ...
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12/23/2021
PyCIL: A Python Toolbox for Class-Incremental Learning
Traditional machine learning systems are deployed under the closed-world...
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12/07/2021
RID-Noise: Towards Robust Inverse Design under Noisy Environments
From an engineering perspective, a design should not only perform well i...
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09/26/2021
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
In cooperative multi-agent reinforcement learning (MARL), where agents o...
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07/27/2021
Co-Transport for Class-Incremental Learning
Traditional learning systems are trained in closed-world for a fixed num...
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07/26/2021
Preliminary Steps Towards Federated Sentiment Classification
Automatically mining sentiment tendency contained in natural language is...
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07/26/2021
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
Although federated learning (FL) has recently been proposed for efficien...
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07/01/2021
Few-Shot Learning with a Strong Teacher
Few-shot learning (FSL) aims to train a strong classifier using limited ...
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06/15/2021
Contextualizing Multiple Tasks via Learning to Decompose
One single instance could possess multiple portraits and reveal diverse ...
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04/17/2021
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport
Complex objects are usually with multiple labels, and can be represented...
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04/08/2021
Few-Shot Action Recognition with Compromised Metric via Optimal Transport
Although vital to computer vision systems, few-shot action recognition i...
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04/08/2021
Support-Target Protocol for Meta-Learning
The support/query (S/Q) training protocol is widely used in meta-learnin...
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04/05/2021
Procrustean Training for Imbalanced Deep Learning
Neural networks trained with class-imbalanced data are known to perform ...
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03/28/2021
Learning Placeholders for Open-Set Recognition
Traditional classifiers are deployed under closed-set setting, with both...
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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...
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02/03/2020
Revisiting Meta-Learning as Supervised Learning
Recent years have witnessed an abundance of new publications and approac...
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01/06/2020
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
We investigate learning a ConvNet classifier with class-imbalanced data....
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06/07/2019
Learning Classifier Synthesis for Generalized Few-Shot Learning
Visual recognition in real-world requires handling long-tailed and even ...
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12/10/2018
