什么是autoencoder？ - 知乎
传统的自编码器（AutoEncoder， AE）包括编码阶段和解码阶段，且拥有对称的结构。 自编码器是一种特殊类型的神经网络，经过训练后可将其输入映射到其输出。自编码器学习压缩数据， …
Autoencoder: using cosine distance as loss function
Sep 10, 2019 · Hey so the Keras implementation of Cosine Similarity is called as Cosine Proximity. It just has one small change, that being cosine proximity = -1*(Cosine Similarity) of …
stable diffusion里的autoencoder和vq vae、vq gan是什么关系?
大名鼎鼎的VQ-VAE（Vector Quantised Variational AutoEncoder），出自谷歌的DeepMind团队，发表于Nips 2017。 VQ-VAE事实上并不是是对于VAE的改进，而就是一个AE ！ 主要 …
How to extract features from the encoded layer of an autoencoder?
Dec 8, 2019 · Therefore, I have implemented an autoencoder using the keras framework in Python. For simplicity, and to test my program, I have tested it against the Iris Data Set, telling …
What is an autoencoder? - Data Science Stack Exchange
Aug 17, 2020 · The autoencoder then works by storing inputs in terms of where they lie on the linear image of . Observe that absent the non-linear activation functions, an autoencoder …
What is the difference between an autoencoder and an encoder …
Jun 18, 2019 · The Wikipedia page for Autoencoder, mentions, The simplest way to perform the copying task perfectly would be to duplicate the signal. Instead, autoencoders are typically …
机器学习11 -- 无监督学习之Auto-Encoder - 知乎
Apr 6, 2024 · 2 Auto-Encoder应用 2.1 文本检索. 自编码器可以用在文本处理上。比如在文本检索领域，经常需要计算query和document的相关性。
what is the main difference between GAN and autoencoder?
Jul 4, 2019 · Summarised: An autoencoder learns to represent some input information very efficiently, and subsequently how to reconstruct the input from it's compressed form. …
Keras - Autoencoder different from Encoder + Decoder
Oct 14, 2019 · The central layer of my Autoencoder is a Dense layer, because I would like to learn it afterwards. My problem is that if I compile and fit the whole Autoencoder, written as …
Using an autoencoder for anomaly detection on categorical data
Say a dataset has 0.5% of its features continuous and 99.5% categorical (binary) with ~2400 features in total. In this dataset, each observation is 1 of 2 classes - Fraud (1) or Not Fraud (0).