Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models

EncodeNet：一种利用熵驱动的广义转换自编码器提升DNN准确率的框架

EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder

本文提出EncodeNet框架，通过广义Converting Autoencoder和类内聚类优化图像特征学习，在保持模型大小的同时将知识转移至轻量级分类器，显著提升了VGG16和ResNet20在CIFAR数据集上的准确率。

面向边缘端低延迟、高能效DNN推理的转换自编码器

A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge

针对边缘设备DNN推理的低延迟高能效需求，提出基于转换自编码器的CBNet框架，可将困难图像转为简单图像后用轻量DNN推理，实验验证其在精度相当的情况下大幅提升速度、降低能耗。

3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder

Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications. In this paper, we propose a new method on the basis of deep learning by formulating the 3D keypoint detection as a regression problem using deep neural network (DNN) with sparse autoencoder (SAE) as our regression model. Both local information and global information of a 3D mesh model in multi-scale space are fully utilized to detect whether a …

掩码自编码器是可扩展的细胞形态学习器

Masked Autoencoders are Scalable Learners of Cellular Morphology

本文研究自监督掩码自编码器在细胞形态学图像上的可扩展性，证明CNN和ViT架构在大规模显微镜数据上训练后显著优于弱监督基线，其中ViT-L/8在推断已知生物学关系时相对提升高达28%。

Extracting temporal features into a spatial domain using autoencoders for sperm video analysis

In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it's capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural…

使用变分自编码器可解释预测直肠癌MRI中的淋巴结转移

Interpretable Prediction of Lymph Node Metastasis in Rectal Cancer MRI Using Variational Autoencoders

针对直肠癌淋巴结转移分期诊断准确性有限的问题，提出使用变分自编码器（VAE）替代大型预训练CNN作为特征编码器，构建了具有解耦结构化潜在空间的VAE-MLP模型，在168名患者MRI数据集上取得SOTA性能，提升了预测的可解释性。