For reliable transmission across a noisy communication channel, classical\nresults from information theory show that it is asymptotically optimal to\nseparate out the source and channel coding processes. However, this\ndecomposition can fall short in the finite bit-length regime, as it requires\nnon...
python3 main.py --datadir=./data --datasource=celebA --channel_model=bsc --noise=0.2 --test_noise=0.2 --n_bits=1000 Citing If you find NECST useful in your research, please consider citing the following paper: @article{choi2018necst, title={Neural Joint Source-Channel Coding}, author=...
NECST:NEURALJOINTSOURCE-CHANNELCODINGKristyChoi†KedarTatwawadi‡TsachyWeissman‡StefanoErmon†StanfordUniversity†DepartmentofComputerScience‡DepartmentofElectricalEngineeringkechoikedarttsachyermon@stanford.eduABSTRACTForreliabletransmissi
Fig. 2: Neural generative coding computation and circuitry. a The two key computation steps taken by an entire NGC network (a GNCN-t2-LΣ) when processing an input (z0 = x): (1) prediction and laterally-weighted error computation, (2) error-correction of neural states. In this dia...
2020-CVPR-Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach 2020-CVPR-Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer 2020-CVPR-The Knowledge Within: Methods for Data-Free Model Compression 2020-CVPR-GAN Compressi...
Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these
The source code of implementing the Bayesian asymmetric quantized neural networks is accessible at https://github.com/NYCU-MLLab. Table 5. Comparison of top 1 accuracy (%), memory reduction ratio, training and test times of using different methods relative to FPNN where ImageNet is used and ...
Finally, a joint module integrates outputs of the DNA and DNase modules to predict the probability that an input sequence is an enhancer. Conv, convolutional layer; Pool, max-pooling layer; Batch norm, batch-normalization layer. DNA module The DNA module is a CNN with multiple convolutional ...
As a consequence of this, they produce very high-quality audio at medium-to-high bitrates, but they tend to introduce coding artifacts when operating at low bitrates.Parametric codecs aim at overcoming this problem by making specific assumptions about the source audio to be encoded (in most ...
(e.g.,sky and ocean), thus are very suitable for the SR training. In the following experiments, apart from using the 91-image dataset for training, we will also evaluate the applicability of the joint set of the General-100 dataset and the 91-image dataset to train our networks. To ...