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pro vyhledávání: '"Ochal, Mateusz"'
In Few-Shot Learning (FSL), models are trained to recognise unseen objects from a query set, given a few labelled examples from a support set. In standard FSL, models are evaluated on query instances sampled from the same class distribution of the su
Externí odkaz:
http://arxiv.org/abs/2408.02052
Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However, a key challenge is identifyin
Externí odkaz:
http://arxiv.org/abs/2203.05469
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard training procedure overlooks the dynamic nature of the real-world whe
Externí odkaz:
http://arxiv.org/abs/2104.05344
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the rea
Externí odkaz:
http://arxiv.org/abs/2101.02523
Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images. Many such methods require hundreds, if not thousands, of images per class to generalize well to unseen examples. Howeve
Externí odkaz:
http://arxiv.org/abs/2005.04621
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a suite of benchmarks for the highly desirable setting of continual few-
Externí odkaz:
http://arxiv.org/abs/2004.11967
Autor:
Stein, Sebastian, Ochal, Mateusz, Moisoiu, Ioana-Adriana, Gerding, Enrico, Ganti, Raghu, He, Ting, La Porta, Tom
We consider an online resource allocation problem where tasks with specific values, sizes and resource requirements arrive dynamically over time, and have to be either serviced or rejected immediately. Reinforcement learning is a promising approach f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______348::44f037419f9356dbc6cb6af1a503e5d5
https://eprints.soton.ac.uk/438382/
https://eprints.soton.ac.uk/438382/
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