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Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation
Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR).
Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compression
End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images.
ParaDiS: Parallelly Distributable Slimmable Neural Networks
While inspired by slimmable networks allowing instant adaptation to resources on just one device, ParaDiS networks consist of several multi-device distributable configurations or switches that strongly share the parameters between them.
SkipW: Resource adaptable RNN with strict upper computational limit
We introduce Skip-Window, a method to allow recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence.
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