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Learning to Compare: Relation Network for Few-Shot Learning
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, calledExpand
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Deep Mutual Learning
Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large network orExpand
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Deeper, Broader and Artier Domain Generalization
The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has aExpand
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Sketch-a-Net that Beats Humans
We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result ofExpand
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Multi-level Factorisation Net for Person Re-identification
Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-IDExpand
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A Markov Clustering Topic Model for mining behaviour in video
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian NetworkExpand
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TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing factsExpand
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Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representationExpand
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Sketch-a-Net: A Deep Neural Network that Beats Humans
We propose a deep learning approach to free-hand sketch recognition that achieves state-of-the-art performance, significantly surpassing that of humans. Our superior performance is a result ofExpand
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Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation
Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semanticExpand
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