Timothy Hospedales

research

∙ 07/20/2023

Label Calibration for Semantic Segmentation Under Domain Shift

Performance of a pre-trained semantic segmentation model is likely to su...

0 Ondrej Bohdal, et al. ∙

research

∙ 07/20/2023

Feed-Forward Source-Free Domain Adaptation via Class Prototypes

Source-free domain adaptation has become popular because of its practica...

0 Ondrej Bohdal, et al. ∙

research

∙ 07/06/2023

Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?

Numerous benchmarks for Few-Shot Learning have been proposed in the last...

0 Luísa Shimabucoro, et al. ∙

research

∙ 06/30/2023

Impact of Noise on Calibration and Generalisation of Neural Networks

Noise injection and data augmentation strategies have been effective for...

0 Martin Ferianc, et al. ∙

research

∙ 06/16/2023

A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning

We propose a novel hierarchical Bayesian model for learning with a large...

0 Minyoung Kim, et al. ∙

research

∙ 06/15/2023

Neural Fine-Tuning Search for Few-Shot Learning

In few-shot recognition, a classifier that has been trained on one set o...

0 Panagiotis Eustratiadis, et al. ∙

research

∙ 05/14/2023

Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning...

14 Raman Dutt, et al. ∙

research

∙ 05/12/2023

Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

Meta-learning and other approaches to few-shot learning are widely studi...

0 Ondrej Bohdal, et al. ∙

research

∙ 05/08/2023

FedHB: Hierarchical Bayesian Federated Learning

We propose a novel hierarchical Bayesian approach to Federated Learning ...

0 Minyoung Kim, et al. ∙

research

∙ 04/16/2023

Fairness in AI and Its Long-Term Implications on Society

Successful deployment of artificial intelligence (AI) in various setting...

0 Ondrej Bohdal, et al. ∙

research

∙ 04/07/2023

ChiroDiff: Modelling chirographic data with Diffusion Models

Generative modelling over continuous-time geometric constructs, a.k.a su...

2 Ayan Das, et al. ∙

research

∙ 03/31/2023

Self-Supervised Multimodal Learning: A Survey

Multimodal learning, which aims to understand and analyze information fr...

0 Yongshuo Zong, et al. ∙

research

∙ 03/25/2023

Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style

This paper studies the problem of zero-short sketch-based image retrieva...

0 Fengyin Lin, et al. ∙

research

∙ 02/24/2023

Amortised Invariance Learning for Contrastive Self-Supervision

Contrastive self-supervised learning methods famously produce high quali...

0 Ruchika Chavhan, et al. ∙

research

∙ 02/23/2023

Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach

We tackle the domain generalisation (DG) problem by posing it as a domai...

0 Minyoung Kim, et al. ∙

research

∙ 12/15/2022

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

Recent image degradation estimation methods have enabled single-image su...

0 Royson Lee, et al. ∙

research

∙ 10/04/2022

MEDFAIR: Benchmarking Fairness for Medical Imaging

A multitude of work has shown that machine learning-based medical diagno...

0 Yongshuo Zong, et al. ∙

research

∙ 08/01/2022

Attacking Adversarial Defences by Smoothing the Loss Landscape

This paper investigates a family of methods for defending against advers...

0 Panagiotis Eustratiadis, et al. ∙

research

∙ 07/17/2022

HyperInvariances: Amortizing Invariance Learning

Providing invariances in a given learning task conveys a key inductive b...

9 Ruchika Chavhan, et al. ∙

research

∙ 07/15/2022

Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention

We study the highly practical but comparatively under-studied problem of...

0 Ondrej Bohdal, et al. ∙

research

∙ 06/15/2022

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

Although deep neural networks are capable of achieving performance super...

18 Adrian El Baz, et al. ∙

research

∙ 04/05/2022

MetaAudio: A Few-Shot Audio Classification Benchmark

Currently available benchmarks for few-shot learning (machine learning w...

0 Calum Heggan, et al. ∙

research

∙ 02/01/2022

Finding lost DG: Explaining domain generalization via model complexity

The domain generalization (DG) problem setting challenges a model traine...

0 Da Li, et al. ∙

research

∙ 11/09/2021

Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation

The meta learning few-shot classification is an emerging problem in mach...

0 Minyoung Kim, et al. ∙

research

∙ 10/26/2021

Defensive Tensorization

We propose defensive tensorization, an adversarial defence technique tha...

4 Adrian Bulat, et al. ∙

research

∙ 10/06/2021

Online Hyperparameter Meta-Learning with Hypergradient Distillation

Many gradient-based meta-learning methods assume a set of parameters tha...

0 Hae Beom Lee, et al. ∙

research

∙ 09/13/2021

Vision-based system identification and 3D keypoint discovery using dynamics constraints

This paper introduces V-SysId, a novel method that enables simultaneous ...

11 Miguel Jaques, et al. ∙

research

∙ 07/15/2021

A Channel Coding Benchmark for Meta-Learning

Meta-learning provides a popular and effective family of methods for dat...

0 Rui Li, et al. ∙

research

∙ 06/19/2021

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

Gradient-based meta-learning and hyperparameter optimization have seen s...

0 Ondrej Bohdal, et al. ∙

research

∙ 06/17/2021

Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error

Calibration of neural networks is a topical problem that is becoming inc...

0 Ondrej Bohdal, et al. ∙

research

∙ 03/29/2021

Cloud2Curve: Generation and Vectorization of Parametric Sketches

Analysis of human sketches in deep learning has advanced immensely throu...

0 Ayan Das, et al. ∙

research

∙ 01/08/2021

Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

Current state-of-the-art few-shot learners focus on developing effective...

0 Xueting Zhang, et al. ∙

research

∙ 12/09/2020

Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation

In reinforcement learning, domain randomisation is an increasingly popul...

0 Chenyang Zhao, et al. ∙

research

∙ 10/17/2020

A Stochastic Neural Network for Attack-Agnostic Adversarial Robustness

Stochastic Neural Networks (SNNs) that inject noise into their hidden la...

0 Panagiotis Eustratiadis, et al. ∙

research

∙ 07/07/2020

Learning to Generate Novel Domains for Domain Generalization

This paper focuses on domain generalization (DG), the task of learning f...

0 Kaiyang Zhou, et al. ∙

research

∙ 07/06/2020

Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows

As data volumes continue to grow, the labelling process increasingly bec...

0 Ivana Balazevic, et al. ∙

research

∙ 07/04/2020

BézierSketch: A generative model for scalable vector sketches

The study of neural generative models of human sketches is a fascinating...

0 Ayan Das, et al. ∙

research

∙ 06/15/2020

Flexible Dataset Distillation: Learn Labels Instead of Images

We study the problem of dataset distillation - creating a small set of s...

0 Ondrej Bohdal, et al. ∙

research

∙ 06/10/2020

A Probabilistic Framework for Discriminative and Neuro-Symbolic Semi-Supervised Learning

In semi-supervised learning (SSL), a rule to predict labels y for data x...

0 Carl Allen, et al. ∙

research

∙ 06/02/2020

NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces

Learning low-dimensional latent state space dynamics models has been a p...

0 Miguel Jaques, et al. ∙

research

∙ 06/01/2020

Latent Domain Learning with Dynamic Residual Adapters

A practical shortcoming of deep neural networks is their specialization ...

0 Lucas Deecke, et al. ∙

research

∙ 05/26/2020

ALBA : Reinforcement Learning for Video Object Segmentation

We consider the challenging problem of zero-shot video object segmentati...

42 Shreyank N Gowda, et al. ∙

research

∙ 05/13/2020

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot design...

22 Stéphane Doncieux, et al. ∙

research

∙ 04/11/2020

Meta-Learning in Neural Networks: A Survey

The field of meta-learning, or learning-to-learn, has seen a dramatic ri...

82 Timothy Hospedales, et al. ∙

research

∙ 04/09/2020

Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation

Domain adaptation (DA) is the topical problem of adapting models from la...

0 Da Li, et al. ∙

research

∙ 04/03/2020

Sequential Learning for Domain Generalization

In this paper we propose a sequential learning framework for Domain Gene...

0 Da Li, et al. ∙

research

∙ 03/12/2020

Deep Domain-Adversarial Image Generation for Domain Generalisation

Machine learning models typically suffer from the domain shift problem w...

8 Kaiyang Zhou, et al. ∙

research

∙ 03/10/2020

Incremental Few-Shot Object Detection

Most existing object detection methods rely on the availability of abund...

17 Juan-Manuel Pérez-Rúa, et al. ∙

research

∙ 03/08/2020

DADA: Differentiable Automatic Data Augmentation

Data augmentation (DA) techniques aim to increase data variability, and ...

20 Yonggang Li, et al. ∙

research

∙ 03/02/2020

Adversarial Generation of Informative Trajectories for Dynamics System Identification

Dynamic System Identification approaches usually heavily rely on the evo...

0 Marija Jegorova, et al. ∙

Timothy Hospedales

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