Can we perform unsupervised domain adaptation without accessing source data? Recent works show that it is not only possible but also very effective. In
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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 on heavy models, that
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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 has been studied mostly
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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 data from previous tasks).
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