Timothy M. Hospedales

Publications164
h-index40
Citations6,299
Highly Influential Citations813
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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, called… Expand
  • 771
  • 173
  • Open Access
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 or… Expand
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  • 45
  • Open Access
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 a… Expand
  • 171
  • 43
  • Open Access
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 of… Expand
  • 150
  • 33
  • Open Access
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-ID… Expand
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  • Open Access
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 Network… Expand
  • 218
  • 29
  • Open Access
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 facts… Expand
  • 73
  • 26
  • Open Access
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 representation… Expand
  • 136
  • 25
  • Open Access
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 of… Expand
  • 130
  • 24
  • Open Access
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 semantic… Expand
  • 179
  • 23
  • Open Access