Timothy M. Hospedales

research

∙ 09/07/2023

Better Practices for Domain Adaptation

Distribution shifts are all too common in real-world applications of mac...

0 Linus Ericsson, et al. ∙

research

∙ 03/25/2023

Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

Deep learning-based recognition systems are deployed at scale for severa...

0 Leonardo Iurada, et al. ∙

research

∙ 12/11/2022

Accelerating Self-Supervised Learning via Efficient Training Strategies

Recently the focus of the computer vision community has shifted from exp...

0 Mustafa Taha Koçyiğit, et al. ∙

research

∙ 06/10/2022

Fisher SAM: Information Geometry and Sharpness Aware Minimisation

Recent sharpness-aware minimisation (SAM) is known to find flat minima w...

0 Minyoung Kim, et al. ∙

research

∙ 04/15/2022

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Few-shot learning (FSL) is an important and topical problem in computer ...

0 Shell Xu Hu, et al. ∙

research

∙ 03/05/2022

Meta Mirror Descent: Optimiser Learning for Fast Convergence

Optimisers are an essential component for training machine learning mode...

0 Boyan Gao, et al. ∙

research

∙ 11/22/2021

Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks

Self-supervised learning is a powerful paradigm for representation learn...

0 Linus Ericsson, et al. ∙

research

∙ 10/18/2021

Self-Supervised Representation Learning: Introduction, Advances and Challenges

Self-supervised representation learning methods aim to provide powerful ...

0 Linus Ericsson, et al. ∙

research

∙ 05/18/2021

Towards Unsupervised Sketch-based Image Retrieval

Current supervised sketch-based image retrieval (SBIR) methods achieve e...

0 Conghui Hu, et al. ∙

research

∙ 03/25/2021

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting

Self-supervised learning has gained prominence due to its efficacy at le...

0 Ayan Kumar Bhunia, et al. ∙

research

∙ 02/27/2021

Searching for Robustness: Loss Learning for Noisy Classification Tasks

We present a "learning to learn" approach for automatically constructing...

0 Boyan Gao, et al. ∙

research

∙ 01/27/2021

FedH2L: Federated Learning with Model and Statistical Heterogeneity

Federated learning (FL) enables distributed participants to collectively...

0 Yiying Li, et al. ∙

research

∙ 12/10/2020

Tensor Composition Net for Visual Relationship Prediction

We present a novel Tensor Composition Network (TCN) to predict visual re...

0 Yuting Qiang, et al. ∙

research

∙ 11/26/2020

How Well Do Self-Supervised Models Transfer?

Self-supervised visual representation learning has seen huge progress in...

0 Linus Ericsson, et al. ∙

research

∙ 07/07/2020

On Learning Semantic Representations for Million-Scale Free-Hand Sketches

In this paper, we study learning semantic representations for million-sc...

7 Peng Xu, et al. ∙

research

∙ 06/22/2020

Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights

In the absence of large labelled datasets, self-supervised learning tech...

0 Linus Ericsson, et al. ∙

research

∙ 03/11/2020

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a va...

21 Wei Zhou, et al. ∙

research

∙ 03/02/2020

Unlimited Resolution Image Generation with R2D2-GANs

In this paper we present a novel simulation technique for generating hig...

38 Marija Jegorova, et al. ∙

research

∙ 02/24/2020

Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the proble...

13 Ayan Kumar Bhunia, et al. ∙

research

∙ 02/21/2020

Fine-Grained Instance-Level Sketch-Based Video Retrieval

Existing sketch-analysis work studies sketches depicting static objects ...

23 Peng Xu, et al. ∙

research

∙ 02/19/2020

Distance-Based Regularisation of Deep Networks for Fine-Tuning

We investigate approaches to regularisation during fine-tuning of deep n...

12 Henry Gouk, et al. ∙

research

∙ 01/08/2020

Deep Learning for Free-Hand Sketch: A Survey and A Toolbox

Free-hand sketches are highly illustrative, and have been widely used by...

0 Peng Xu, et al. ∙

research

∙ 10/17/2019

Deep clustering with concrete k-means

We address the problem of simultaneously learning a k-means clustering a...

14 Boyan Gao, et al. ∙

research

∙ 09/25/2019

On Understanding Knowledge Graph Representation

Many methods have been developed to represent knowledge graph data, whic...

10 Carl Allen, et al. ∙

research

∙ 07/29/2019

Goal-Driven Sequential Data Abstraction

Automatic data abstraction is an important capability for both benchmark...

3 Umar Riaz Muhammad, et al. ∙

research

∙ 07/14/2019

Measuring the Transferability of Adversarial Examples

Adversarial examples are of wide concern due to their impact on the reli...

0 Deyan Petrov, et al. ∙

research

∙ 05/09/2019

Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice

Contemporary person re-identification (Re-ID) methods usually require ac...

6 Jieru Jia, et al. ∙

research

∙ 02/19/2019

Investigating Generalisation in Continuous Deep Reinforcement Learning

Deep Reinforcement Learning has shown great success in a variety of cont...

8 Chenyang Zhao, et al. ∙

research

∙ 01/31/2019

Episodic Training for Domain Generalization

Domain generalization (DG) is the challenging and topical problem of lea...

10 Da Li, et al. ∙

research

∙ 01/31/2019

Feature-Critic Networks for Heterogeneous Domain Generalization

The well known domain shift issue causes model performance to degrade wh...

10 Yiying Li, et al. ∙

research

∙ 01/28/2019

TuckER: Tensor Factorization for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts. How...

6 Ivana Balazevic, et al. ∙

research

∙ 12/06/2018

Disjoint Label Space Transfer Learning with Common Factorised Space

In this paper, a unified approach is presented to transfer learning that...

2 Xiaobin Chang, et al. ∙

research

∙ 11/17/2018

Deep Comparison: Relation Columns for Few-Shot Learning

Few-shot deep learning is a topical challenge area for scaling visual re...

0 Xueting Zhang, et al. ∙

research

∙ 09/29/2018

Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice

Active learning aims to reduce annotation cost by predicting which sampl...

0 Kunkun Pang, et al. ∙

research

∙ 08/21/2018

Hypernetwork Knowledge Graph Embeddings

Knowledge graphs are large graph-structured databases of facts, which ty...

0 Ivana Balazevic, et al. ∙

research

∙ 08/07/2018

Deep Factorised Inverse-Sketching

Modelling human free-hand sketches has become topical recently, driven b...

0 Kaiyue Pang, et al. ∙

research

∙ 08/07/2018

Universal Perceptual Grouping

In this work we aim to develop a universal sketch grouper. That is, a gr...

8 Ke Li, et al. ∙

research

∙ 06/19/2018

Deep Neural Decision Trees

Deep neural networks have been proven powerful at processing perceptual ...

0 Yongxin Yang, et al. ∙

research

∙ 04/30/2018

Sketch-a-Classifier: Sketch-based Photo Classifier Generation

Contemporary deep learning techniques have made image recognition a reas...

0 Conghui Hu, et al. ∙

research

∙ 04/13/2018

Learning Deep Sketch Abstraction

Human free-hand sketches have been studied in various contexts including...

0 Umar Riaz Muhammad, et al. ∙

research

∙ 04/04/2018

SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval

We propose a deep hashing framework for sketch retrieval that, for the f...

0 Peng Xu, et al. ∙

research

∙ 03/24/2018

Multi-Level Factorisation Net for Person Re-Identification

Key to effective person re-identification (Re-ID) is modelling discrimin...

0 Xiaobin Chang, et al. ∙

research

∙ 03/16/2018

Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool

In recent years, visual question answering (VQA) has become topical. The...

0 Feng Liu, et al. ∙

research

∙ 11/22/2017

The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching

Many vision problems require matching images of object instances across ...

0 Qian Yu, et al. ∙

research

∙ 11/16/2017

Deep Matching Autoencoders

Increasingly many real world tasks involve data in multiple modalities o...

0 Tanmoy Mukherjee, et al. ∙

research

∙ 11/16/2017

Learning to Compare: Relation Network for Few-Shot Learning

We present a conceptually simple, flexible, and general framework for fe...

0 Flood Sung, et al. ∙

research

∙ 10/10/2017

iVQA: Inverse Visual Question Answering

In recent years, visual question answering (VQA) has become topical as a...

0 Feng Liu, et al. ∙

research

∙ 10/09/2017

Deeper, Broader and Artier Domain Generalization

The problem of domain generalization is to learn from multiple training ...

0 Da Li, et al. ∙

research

∙ 08/08/2017

Weakly Supervised Image Annotation and Segmentation with Objects and Attributes

We propose to model complex visual scenes using a non-parametric Bayesia...

0 Zhiyuan Shi, et al. ∙

research

∙ 07/30/2017

Deep Multi-View Learning with Stochastic Decorrelation Loss

Multi-view learning aims to learn an embedding space where multiple view...

0 Xiaobin Chang, et al. ∙

Timothy M. Hospedales

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