In this paper, we propose to simultaneously learn appearance and motion to obtain fine-grained spatio-temporal patterns. To this end, we present an adversarial 3D convolutional auto-encoder to learn ...

We formulate this problem as training a deep auto-encoder system with an adversarial convolutional layer and propose a training procedure with mini-batches of input samples and channels. Instead of ...

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multi-phase flow in heterogeneous random media . uncertainty-quantification time-dependent multi-phase-flows ...

Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are unable to ...

Convolutional encoder outputs are not only associated with the encode elements at present, but also affected by several ones before. (n, k, m) is used for describing convolutional codes, where k are ...

Lattice’s Block Convolutional Encoder IP core is a parameterizable core for convolutional encoding of continuous or burst input data streams. The core allows different code rates and constraint ...

Convolutional encoding is a process of adding redundancy to a signal stream. Lattice's Convolutional Encoder core is a parameterizable core for convolutional encoding of a continuous input data stream ...

convolutional-encoder Star Here are 3 public repositories matching this topic... Language: All. Filter by language. All 3 C++ 1 Prolog 1 VHDL 1. williamyang98 / ViterbiDecoderCpp. Star 12. Code Issues ...