Web-scale training for face identification. Interjection words are generally used at the beginning of a sentence. Learning to compare: Relation network for few-shot learning. An interjection is one of the parts of speech used to express a particular emotion or sentiment (strong feeling or sudden emotion like surprise, joy, excitement, disgust, enthusiasm, sorrow, approval, calling, attention, etc) of the speaker to the reader. Prototypical networks for few-shot learning. Facenet: A unified embedding for face recognition and clustering. Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement. Weinberger, editors, ICML, volume 48 of Proceedings of Machine Learning Research, pages 1842-1850, New York, New York, USA, 20. Meta-learning with memory-augmented neural networks. Meta-learning for semi-supervised few-shot classification. Optimization as a model for few-shot learning. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, pages 44-53, 2017. When is multitask learning effective? Semantic sequence prediction under varying data conditions. Film: Visual reasoning with a general conditioning layer. Learning visual reasoning without strong priors. Rapid adaptation with conditionally shifted neurons. Human-level concept learning through probabilistic program induction. One-shot learning by inverting a compositional causal process. Learning multiple layers of features from tiny images. In ICML Deep Learning Workshop, volume 2, 2015. Siamese neural networks for one-shot image recognition. In NIPS Deep Learning and Representation Learning Workshop, 2015. Distilling the knowledge in a neural network. Deep residual learning for image recognition. Dimensionality reduction by learning an invariant mapping. Model-agnostic meta-learning for fast adaptation of deep networks. Object classification from a single example utilizing class relevance metrics. A learned representation for artistic style. Discriminative k-shot learning using probabilistic models. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Few-shot learning has become essential for producing models that generalize from few examples.
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