2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…