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Meta-learning the Learning Trends Shared Across Tasks
Meta-learning stands for 'learning to learn' such that generalization to...
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Self-supervised Knowledge Distillation for Few-shot Learning
Real-world contains an overwhelmingly large number of object classes, le...
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A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
Counterfeit apps impersonate existing popular apps in attempts to misgui...
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iTAML: An Incremental Task-Agnostic Meta-learning Approach
Humans can continuously learn new knowledge as their experience grows. I...
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Incremental Object Detection via Meta-Learning
In a real-world setting, object instances from new classes may be contin...
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TimeCaps: Capturing Time Series Data with Capsule Networks
Capsule networks excel in understanding spatial relationships in 2D data...
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Random Path Selection for Incremental Learning
Incremental life-long learning is a main challenge towards the long-stan...
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DeepCaps: Going Deeper with Capsule Networks
Capsule Network is a promising concept in deep learning, yet its true po...
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TextCaps : Handwritten Character Recognition with Very Small Datasets
Many localized languages struggle to reap the benefits of recent advance...
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Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos
Activity recognition in videos in a deep-learning setting---or otherwise...
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A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
Counterfeit apps impersonate existing popular apps in attempts to misgui...
read it