Category: transfer learning
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Source-free unsupervised domain adaptation
Can we perform unsupervised domain adaptation without accessing source data? Recent works show that it is not only possible but ...
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MAE, SlimCAE and DANICE: towards practical neural image compression
Neural image and video codecs achieve competitive rate-distortion performance. However, they have a series of practical limitations, such as relying ...
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Mix and match networks (part 2)
This is a brief update on mix and match networks (M&MNets), describing the new ideas included in the extended version ...
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MeRGANs: generating images without forgetting
The problem of catastrophic forgetting (a network forget previous tasks when learning a new one) and how to address it ...
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Learning RGB-D features for images and videos
Depth sensors capture information that complements conventional RGB data. How to combine them in an effective multimodal representation is still ...
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Mix and match networks
We recently explored how we can take multiple seen image-to-image translators and reuse them to infer other unseen translations, in ...
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Rotating networks to prevent catastrophic forgetting
In contrast to humans, neural networks tend to quickly forget previous tasks when trained on a new one (without revisiting ...