Search Results for author:
Found 16 papers, 6 papers with code
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting
Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration.
On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data.
Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information about the material composition of the imaged scene.
Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior
It is shown that LRS-PnP is able to predict missing pixels and bands even when all spectral bands of the image are missing.
MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets.
Ranked #1 on
Few-Shot Audio Classification
on Common Voice
(using extra training data)
Amortised Invariance Learning for Contrastive Self-Supervision
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
MetaAudio: A Few-Shot Audio Classification Benchmark
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.
Ranked #1 on
Few-Shot Audio Classification
on NSynth
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots.
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
OMASGAN addresses the rarity of anomalies by generating strong and adversarial OoD samples on the distribution boundary using only normal class data, effectively addressing mode collapse.
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection
We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models, including classification.
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary.
Generative Adversarial Network
Unsupervised Anomaly Detection
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
We propose an invertible-residual-network-based model, the Boundary of Distribution Support Generator (BDSG).
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks
Compressed sensing (CS) leverages the sparsity prior to provide the foundation for fast magnetic resonance imaging (fastMRI).
DeepMP for Non-Negative Sparse Decomposition
Non-negative signals form an important class of sparse signals.
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction
Non-negative signals form an important class of sparse signals.
Dictionary Subselection Using an Overcomplete Joint Sparsity Model
As this model is often unknown for many classes of the signals, we need to select such a model based on the domain knowledge or using some exemplar signals.
