arXiv:2101.10657
공식 코드 · Papers with Code
Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
원문 초록
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
함께 살펴볼 논문
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Iterative Quantum Feature Maps
Performing Image Classification for 10 Different Monkey Species using CNN
Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor
토론
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