Connectionist Temporal Classification(CTC)是一种用于序列学习问题的损失函数,特别是在输出序列长度与输入序列长度不一致的情况下。CTC允许模型在不需要事先对齐输入和输出序列的情况下进行训练,这在语音识别、手写识别等任务中尤为重要。CTC通过在输出序列中引入一个“空白”标签(blank label)来处理不同长度的对齐问题。
1. Re:CTC (Connectionist Temporal Classification) 算法原理 讲得很清楚,但是图11上面的αt3(2)公式似乎有错误,但是后面的计算结果是正确的,似乎属于笔误 --今淇 2. Re:[问题] docker: Failed to start Docker Application Container Engine. 谢谢大牛,fd:// 替换为 unix:// ,这什么区别呢 --liangguohao...
Demystifying the Connectionist Temporal Classification Loss Background: Speech Recognition Pipelines Typical speech processing approaches use a deep learning component (either a CNN or an RNN) followed by a mechanism to ensure that there’s consistency in time (traditionally an HMM). the deep laerning...
最小编辑距离的实现就是一个递归,如果读者不懂,可以自行百度了。 Connectionist Temporal Classification 一个CTC网络有一个softmax输出层,出了序列的输出外,还增加了一个额外的输出单元,最开始激励的|L|个单元被解释成在这个时刻对应标签的观察概率,激励的额外的单元是一个空白的观察概率或者无标签的观察概率。这些输...
Connectionist temporal classification loss for vector quantized variational autoencoder in zero-shot voice conversionVoice conversionZero-shotVQ-VAEConnectionist temporal classificationVector quantized variational autoencoder (VQ-VAE) has recently become an increasingly popular method in non-parallel zero-shot ...
论文笔记:Connectionist Temporal Classification: Labelling Unsegmented Sequence,程序员大本营,技术文章内容聚合第一站。
论文地址:https://papers.nips.cc/paper/7363-connectionist-temporal-classification-with-maximum-entropy-regularization.pdf https://zhuanlan.zhihu.com/p/82302872 CTC的问题: 1、容易陷入局部最优 2、尖峰分布 作者认为peak distribution是一种过拟合的表现, ...
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning (pp. 369-376). ACM. 1. 背景 任务:sequence labelling, unsegmented input。 传统解决方案:图模型(HMM、CRF)。缺陷:需要领域知识(...
In this work, we propose a segmentation-free OCR model for text captcha classification based on the connectionist temporal classification loss technique. The proposed model is trained and tested on a publicly available captcha dataset. The proposed model gives 99.80\% character level accuracy, while ...
References [1] Word Beam Search: A CTC Decoding Algorithm [2] Beam Search Decoding in CTC-trained Neural Networks [3] Scheidl - Handwritten Text Recognition in Historical Documents [4] Scheidl - Word Beam Search: A Connectionist Temporal Classification Decoding AlgorithmAbout...