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07/20/2023
Label Calibration for Semantic Segmentation Under Domain Shift
Performance of a pre-trained semantic segmentation model is likely to su...
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07/20/2023
Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Source-free domain adaptation has become popular because of its practica...
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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...
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06/30/2023
Impact of Noise on Calibration and Generalisation of Neural Networks
Noise injection and data augmentation strategies have been effective for...
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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...
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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...
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05/14/2023
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning...
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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...
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05/08/2023
FedHB: Hierarchical Bayesian Federated Learning
We propose a novel hierarchical Bayesian approach to Federated Learning ...
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04/16/2023
Fairness in AI and Its Long-Term Implications on Society
Successful deployment of artificial intelligence (AI) in various setting...
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04/07/2023
ChiroDiff: Modelling chirographic data with Diffusion Models
Generative modelling over continuous-time geometric constructs, a.k.a su...
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03/31/2023
Self-Supervised Multimodal Learning: A Survey
Multimodal learning, which aims to understand and analyze information fr...
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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...
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02/24/2023
Amortised Invariance Learning for Contrastive Self-Supervision
Contrastive self-supervised learning methods famously produce high quali...
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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...
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12/15/2022
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Recent image degradation estimation methods have enabled single-image su...
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10/04/2022
MEDFAIR: Benchmarking Fairness for Medical Imaging
A multitude of work has shown that machine learning-based medical diagno...
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08/01/2022
Attacking Adversarial Defences by Smoothing the Loss Landscape
This paper investigates a family of methods for defending against advers...
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07/17/2022
HyperInvariances: Amortizing Invariance Learning
Providing invariances in a given learning task conveys a key inductive b...
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07/15/2022
Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention
We study the highly practical but comparatively under-studied problem of...
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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...
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04/05/2022
MetaAudio: A Few-Shot Audio Classification Benchmark
Currently available benchmarks for few-shot learning (machine learning w...
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02/01/2022
Finding lost DG: Explaining domain generalization via model complexity
The domain generalization (DG) problem setting challenges a model traine...
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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...
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10/26/2021
Defensive Tensorization
We propose defensive tensorization, an adversarial defence technique tha...
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10/06/2021
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters tha...
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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 ...
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07/15/2021
A Channel Coding Benchmark for Meta-Learning
Meta-learning provides a popular and effective family of methods for dat...
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06/19/2021
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Gradient-based meta-learning and hyperparameter optimization have seen s...
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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...
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03/29/2021
Cloud2Curve: Generation and Vectorization of Parametric Sketches
Analysis of human sketches in deep learning has advanced immensely throu...
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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...
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12/09/2020
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation
In reinforcement learning, domain randomisation is an increasingly popul...
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10/17/2020
A Stochastic Neural Network for Attack-Agnostic Adversarial Robustness
Stochastic Neural Networks (SNNs) that inject noise into their hidden la...
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07/07/2020
Learning to Generate Novel Domains for Domain Generalization
This paper focuses on domain generalization (DG), the task of learning f...
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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...
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07/04/2020
BézierSketch: A generative model for scalable vector sketches
The study of neural generative models of human sketches is a fascinating...
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06/15/2020
Flexible Dataset Distillation: Learn Labels Instead of Images
We study the problem of dataset distillation - creating a small set of s...
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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...
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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...
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06/01/2020
Latent Domain Learning with Dynamic Residual Adapters
A practical shortcoming of deep neural networks is their specialization ...
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05/26/2020
ALBA : Reinforcement Learning for Video Object Segmentation
We consider the challenging problem of zero-shot video object segmentati...
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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...
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04/11/2020
Meta-Learning in Neural Networks: A Survey
The field of meta-learning, or learning-to-learn, has seen a dramatic ri...
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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...
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04/03/2020
Sequential Learning for Domain Generalization
In this paper we propose a sequential learning framework for Domain Gene...
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03/12/2020
Deep Domain-Adversarial Image Generation for Domain Generalisation
Machine learning models typically suffer from the domain shift problem w...
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03/10/2020
Incremental Few-Shot Object Detection
Most existing object detection methods rely on the availability of abund...
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03/08/2020
DADA: Differentiable Automatic Data Augmentation
Data augmentation (DA) techniques aim to increase data variability, and ...
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03/02/2020