COMPARATIVE STUDY OF DEEP LEARNING MODELS IN MULTI-LABEL SCENE CLASSIFICATION

Keyphrases

• AlexNet 50%
• Art Studies 50%
• Classification Results 50%
• Computer Vision Applications 50%
• Deep Learning Model 100%
• DenseNet121 50%
• Domain Shift 50%
• Economic Time 50%
• Economic Utility 50%
• Environmental Monitoring 50%
• Human Interpretation 50%
• Land Use 50%
• Land Use Classification 50%
• Land Use Data 100%
• Machine Learning Algorithms 50%
• Meaning of Land 50%
• Multi-class 50%
• Multi-class Classification 50%
• Open Dataset 50%
• Overall Accuracy 50%
• Remote Sensing Image 50%
• Robust Utility 50%
• Scene Classification 100%
• SqueezeNet 50%
• Time Utility 50%

Computer Science

• classification result 50%
• Computer Vision Applications 50%
• Deep Learning Model 100%
• DenseNet 50%
• Machine Learning Algorithm 50%
• Multi Class Classification 50%
• Multiclass Classification 50%
• Open Data 50%

Earth and Planetary Sciences

• Computer Vision 33%
• Environmental Monitoring 33%
• Land Use 100%
• Land Use Classification 33%
• Machine Learning 33%
• Scene Classification 100%
• State of the Art 33%