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Found 9 papers, 4 papers with code
SIESTA: Efficient Online Continual Learning with Sleep
Compared to REMIND and prior arts, SIESTA is far more computationally efficient, enabling continual learning on ImageNet-1K in under 2 hours on a single GPU; moreover, in the augmentation-free setting it matches the performance of the offline learner, a milestone critical to driving adoption of continual learning in real-world applications.
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
We found that input perturbation and temperature scaling yield the best performance on large scale datasets regardless of the feature space regularization strategy.
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i. e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i. e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success.
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
Continual Lifelong Learning with Neural Networks: A Review
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan.
FearNet: Brain-Inspired Model for Incremental Learning
Arguably, the best method for incremental class learning is iCaRL, but it requires storing training examples for each class, making it challenging to scale.
Measuring Catastrophic Forgetting in Neural Networks
Deep neural networks are used in many state-of-the-art systems for machine perception.
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery.
High-Resolution Multispectral Dataset for Semantic Segmentation
Unmanned aircraft have decreased the cost required to collect remote sensing imagery, which has enabled researchers to collect high-spatial resolution data from multiple sensor modalities more frequently and easily.
