Feb 6, 2021 · Convolutional neural network (CNN)-based encoder-decoder models have profoundly inspired recent works in the field of salient object detection (SOD).

Apr 5, 2025 · SegNet is a deep learning architecture designed for semantic segmentation, where the goal is to classify each pixel in an image into a predefined category. It is an encoder-decoder neural network tailored for pixel-wise image segmentation, making it highly effective for tasks that require detailed and precise segmentation of images.

Apr 6, 2023 · A Convolutional (CNN/CNN)-based Encoder-Decoder Neural Network is an encoder-decoder neural network that consists of a encoder neural network and a decoder neural network in which one or both are convolutional neural …

Dec 13, 2023 · In this study, a U-net-based deep convolutional encoder–decoder network was developed for predicting high-resolution (256 × 256) optimized structures using transfer learning and fine-tuning for topology optimization.

Sep 1, 2021 · We design and implement convolutional neural network (CNN) based modified residual U-Net for semantic segmentation of plants from the background. We also use SegNet and U-Net architectures for comparison purpose.

Apr 4, 2024 · In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module.

Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting Abstract: In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting.

Abstract—In this paper, we propose two modified neural net-works based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN).

1 day ago · In Supplementary Note 2, we show how by increasing the number of convolutional and max-pooling layers successively, in our encoder-decoder CNN [Supplementary Table 1], increases the accuracy of ...

2 days ago · The machine learning model, which we named physics-informed convolutional encoder-decoder (PICED), integrates a convolutional neural network (CNN)-based encoder-decoder model with a physics-informed neural network (PINN). Using this approach, three-dimensional stress fields can be predicted with high accuracy for multiple interpolated data ...