102 citations · 130 across the 16 of their papers we have counts for
18 papers
Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpainting
Shuo Li, Mike Davies, Mehrdad Yaghoobi
A novel Hyperspectral diffusion Equivariant Imaging (HyDiff-EI) framework for solving the hyperspectral image (HSI) inpainting problem has been presented here. Unlike conventional…
Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
Shuo Li, Mehrdad Yaghoobi
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene.…
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting
Shuo Li, Mike Davies, Mehrdad Yaghoobi
Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology f…
On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
Calum Heggan, Sam Budgett, Timothy Hospedales +1
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. Networks pretrained through self-supervision s…
Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior
Shuo Li, Mehrdad Yaghoobi
Hyperspectral Image (HSI)s cover hundreds or thousands of narrow spectral bands, conveying a wealth of spatial and spectral information. However, due to the instrumental errors and…
Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations
Shuo Li, Mehrdad Yaghoobi
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information about the material composition of the imaged scene. How…
MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
Calum Heggan, Tim Hospedales, Sam Budgett +1
Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets. A key reason that these powerful f…
Amortised Invariance Learning for Contrastive Self-Supervision
Ruchika Chavhan, Henry Gouk, Jan Stuehmer +3
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances establi…
MetaAudio: A Few-Shot Audio Classification Benchmark
Calum Heggan, Sam Budgett, Timothy Hospedales +1
Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification…
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and…
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models trained in an unsupervised manner may set high likelihood and low reconstruction loss to Out-of-Distribution (OoD) samples. This increases Type II errors and lead…
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative Adversarial Networks (GAN) are a powerful methodology and can be used for unsupervised anomaly detection, where current techniques have limitations such as the accurate…
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist p…
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks
Jingshuai Liu, Mehrdad Yaghoobi
Compressed sensing (CS) leverages the sparsity prior to provide the foundation for fast magnetic resonance imaging (fastMRI). However, iterative solvers for ill-posed problems hind…
DeepMP for Non-Negative Sparse Decomposition
Konstantinos A. Voulgaris, Mike E. Davies, Mehrdad Yaghoobi
Non-negative signals form an important class of sparse signals. Many algorithms have already beenproposed to recover such non-negative representations, where greedy and convex rela…
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction
Konstantinos Voulgaris, Mike E. Davies, Mehrdad Yaghoobi
Non-negative signals form an important class of sparse signals. Many algorithms have already beenproposed to recover such non-negative representations, where greedy and convex rela…
Dictionary Subselection Using an Overcomplete Joint Sparsity Model
Mehrdad Yaghoobi, Laurent Daudet, Michael E. Davies
Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal…
Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling
Mehrdad Yaghoobi, Sangnam Nam, Remi Gribonval +1
We consider the problem of learning a low-dimensional signal model from a collection of training samples. The mainstream approach would be to learn an overcomplete dictionary to pr…