Abstract: Convolutional neural networks (CNNs) are widely adopted for remote sensing image scene classification. However, labeling of large annotated remote sensing datasets is costly and time ...
Abstract: Multitask learning with a pretext task has excelled in time-series classification task lacking labeled data. The key to multitask learning is to build a pretext task and learn the most ...
We independently review everything we recommend. We may get paid to link out to retailer sites, and when you buy through our links, we may earn a commission. Learn more› By Matthew Guay It’s hard not ...
Currently, in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR), few-shot methods can save cost and resources while enhancing adaptability. However, due to the limitations of SAR imaging ...
PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...
Large Language Models (LLMs) have shown impressive performance in a range of tasks in recent years, especially classification tasks. These models demonstrate amazing performance when given gold labels ...
Background: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of ...
In machine learning, multi-task learning (MTL) has emerged as a powerful paradigm that enables concurrent training of multiple interrelated algorithms. By exploiting the inherent connections between ...
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