Transfer learning for Latin and Chinese characters with Deep Neural Networks

By Dan Claudiu Ciresan, Ueli Meier, J. Urgen Schmidhuber. 2012

214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Learning: Methods and Applications (2014, methodology): “[58] applied the architecture of deep CNNs to character recognition tasks for Latin and for Chinese.”
Neurogenesis deep learning: Extending deep networks to accommodate new classes (2016, background): “DNNs have found some successes in transfer learning, due to their general-purpose feature detectors at shallow layers of a network [5][6], but our focus is on situations where that is not the case.”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “Supervised Transfer Learning from one dataset to another [32, 43] can speed up learning.”
Deep learning for visual understanding: A review (2016, methodology): “It is an emerging approach and has been widely applied in traditional artificial intelligence domains, such as semantic parsing [1], transfer learning [2,3], natural language processing [4], computer vision [5,6] and many more.”
On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks (2016, methodology): “The authors in [9] use a CNN to learn Chinese characters from Latin and the other way around.”
Pooling Methods in Deep Neural Networks, a Review (2020, methodology): “It is an emerging approach and has been widely applied in traditional artificial intelligence domains, such as semantic parsing [1], transfer learning [2, 3], natural language processing [4], computer vision [5, 6] and many more.”
With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning (2018, background): “In a deep learning context, transfer learning has been shown to be effective in vision [18, 52, 51, 15], speech [34, 63, 30, 20], and text [33, 40].”
Latent Backdoor Attacks on Deep Neural Networks (2019, background): “In a deep learning context, transfer learning has been shown to be effective in vision [5, 11, 42, 43], speech [13, 20, 24, 51], and text [22, 34].”
Multilingual acoustic models using distributed deep neural networks (2013, background): “Transfer learning [27] refers to the situation where the network of a target language is initialized with an existing source network, for example, by pre-training or fine-tuning [21, 22, 19, 23], and is not trained from scratch.”
Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification (2013, background): “It contains 224419 characters written by 60 persons.”
My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 (2013, methodology): “…are adaptations and extensions of an over 40-year-old algorithm, namely, supervised efﬁ-cient backprop [59, 90] (TL 1.2) (compare [60, 53, 75, 12, 67]) or BPTT/RTRL for RNN, e.g., [94, 73, 91, 68, 80, 61] (exceptions include two 2011 contests specialised on transfer learning [35]—but compare [23]).”
An introduction to computational networks and the computational network toolkit (invited talk) (2014, background): “International Conference on Neural Networks (IJCNN), 2012, pp. 1–6.”
Transfer learning using computational intelligence: A survey (2015, background): “Knowledge transfer between different handwritten character recognition tasks [48] is another kind of application of transfer learning in computer vision.”