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Found 10 papers, 4 papers with code
Exemplar-Free Class Incremental Learning via Incremental Representation
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any old-class exemplars (i. e., samples).
E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network
In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks.
CLIP-KD: An Empirical Study of Distilling CLIP Models
CLIP has become a promising language-supervised visual pre-training framework and achieves excellent performance over a wide range of tasks.
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation
Most successful CIL methods incrementally train a feature extractor with the aid of stored exemplars, or estimate the feature distribution with the stored prototypes.
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability.
TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving.
Lifelong Generative Learning via Knowledge Reconstruction
Generative models often incur the catastrophic forgetting problem when they are used to sequentially learning multiple tasks, i. e., lifelong generative learning.
Fast Convolution based on Winograd Minimum Filtering: Introduction and Development
Convolution operators are the fundamental component of convolutional neural networks, and it is also the most time-consuming part of network training and inference.
Multi-Scale Cost Volumes Cascade Network for Stereo Matching
This is because different cost volumes play a crucial role in balancing speed and accuracy.
A Unified Model of Feature Extraction and Clustering for Spike Sorting
Finally, taking the best of the clustering validity indices into the proposed model, we derive an automatic spike sorting method.
Signal Processing
