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Found 18 papers, 15 papers with code
TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen
The era of pre-trained models has ushered in a wealth of new insights for the machine learning community.
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
Despite the strong performance of Pre-Trained Models (PTMs) in CIL, a critical issue persists: learning new classes often results in the overwriting of old ones.
Continual Learning with Pre-Trained Models: A Survey
Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves.
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.
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
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 with streaming data.
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.
Learning without Forgetting for Vision-Language Models
While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning generalizable representations with the aid of textual information.
Preserving Locality in Vision Transformers for Class Incremental Learning
In this paper, we encourage the model to preserve more local information as the training procedure goes on and devise a Locality-Preserved Attention (LPA) layer to emphasize the importance of local features.
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
ADAM is a general framework that can be orthogonally combined with any parameter-efficient tuning method, which holds the advantages of PTM's generalizability and adapted model's adaptivity.
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.
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.
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
The ability to learn new concepts continually is necessary in this ever-changing world.
Ranked #1 on
Incremental Learning
on ImageNet100 - 20 steps
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset.
Ranked #5 on
Few-Shot Class-Incremental Learning
on CIFAR-100
Forward Compatible Few-Shot Class-Incremental Learning
Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes.
Ranked #4 on
Few-Shot Class-Incremental Learning
on CIFAR-100
PyCIL: A Python Toolbox for Class-Incremental Learning
Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process.
Co-Transport for Class-Incremental Learning
As a result, we propose CO-transport for class Incremental Learning (COIL), which learns to relate across incremental tasks with the class-wise semantic relationship.
Contextualizing Meta-Learning via Learning to Decompose
To this end, we propose Learning to Decompose Network (LeadNet) to contextualize the meta-learned ``support-to-target'' strategy, leveraging the context of instances with one or mixed latent attributes in a support set.
Learning Placeholders for Open-Set Recognition
To this end, we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which prepares for the unknown classes by allocating placeholders for both data and classifier.
