People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

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Human-level concept learning through probabilistic program induction.

Handwritten characters drawn by a model Not only do children learn effortlessly, they do so quickly and with a remarkable ability to use what they have learned as the raw material for creating new stuff. Lake et al. describe a computational model that learns in a similar fashion and does so better than current deep learning algorithms. The model classifies, parses, and recreates handwritten characters, and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce. Science, this issue p. 1332 Combining the capacity to handle noise with probabilistic learning yields humanlike performance in a computational model. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior.

Supplementary Material for Human-level concept learning through probabilistic program induction

Fisherpalms based palmprint recognition

http://machine-learning.martinsewell.com/ensembles/LamSuen1995.pdf

Optimal combinations of pattern classifiers

People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems. Here we present a Hierarchical Bayesian model based on com-positionality and causality that can learn a wide range of natural (although simple) visual concepts, generalizing in human-like ways from just one image. We evaluated performance on a challenging one-shot classification task, where our model achieved a human-level error rate while substantially outperforming two deep learning models. We also tested the model on another conceptual task, generating new examples, by using a "visual Turing test" to show that our model produces human-like performance.

/pdf/one-shot-learning-by-inverting-a-compositional-causal-4ezk819ejp.pdf

One-shot learning by inverting a compositional causal process

This paper describes techniques for stroke extraction used in the recognition of handwritten Chinese characters. A new set of feature points is proposed for the analysis of skeleton images. Based on a geometrical graph, a novel criterion is proposed for the identification of fork points in a skeleton image which correspond to joint points in the original character image. Experimental results indicate that the proposed method correctly determines the fork points, and is effective in unifying the joint points.

Identification of fork points on the skeletons of handwritten Chinese characters

Presents a robust thinning-based method for the segmentation of strokes from handwritten Chinese characters. A new set of feature points is proposed for the analysis of skeleton images. A geometrical graph-based approach is developed for the analysis of strokes. A novel criterion is proposed for the identification of the fork points in a skeleton image which correspond to the same joint points in the original character image. Experimental results show that the proposed method is effective.

Robust stroke segmentation method for handwritten Chinese character recognition

A novel approach is proposed for the extraction of legal and courtesy amounts and date from cheque images based on the structural description of cheques. A method for the representation of cheques is presented. Several image processing techniques and algorithms have been developed in this approach. Experimental results show that the approach is effective and the proposed techniques and algorithms perform well.

Automatic extraction of items from cheque images for payment recognition

A classification method has been proposed to recognize images of multiple classes based on algebraic feature extraction and classifier combining techniques. First, an image algebraic feature extraction method is applied to all pairs of classes to extract the image features. Then, a nearest neighbor classifier with a small number of prototypes is designed for each pair of classes based on the algebraic features of training samples. Finally, a neural network technique is used to combine the measurement values of paired classes. Experiments on the U.S. zip code data base show that the method is effective.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image classification by classifier combining technique

One of the most important topics in handwritten character recognition is the extraction of features from character images. In this paper, an algebraic feature extraction technique is applied to recognize handwritten characters. The discriminant performance of the algebraic features extracted from both handprinted characters and totally unconstrained handwritten numerals is studied. Experimental results are provided.

Ke Liu

Papers

Identification of fork points on the skeletons of handwritten Chinese characters

Robust stroke segmentation method for handwritten Chinese character recognition

Automatic extraction of items from cheque images for payment recognition

Image classification by classifier combining technique

Discriminant performance of the algebraic features of handwritten character images