Paper:
One-shot learning by inverting a compositional causal process
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 compositionality 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.

Review:
Reviewer response to rebuttal: I have read 
      through the author's rebuttal and I am happy with the proposed changes. I 
      have not changed my review as I already recommended this paper for 
      acceptance. Previous Review: In this work, the authors 
      develop a hierarchical generative model for producing and classifying 
      written characters with the goal of achieving a high level of performance 
      with just one training example. The model is rooted in learning the 
      compositional structure of characters and the causal relationship that 
      dictates how characters are produced. The model is compared to a simpler 
      version of the model that does not represent character strokes, a deep 
      boltzmann machine approach, and a hierarchical deep learning method. In 
      addition to significantly outperforming these comparison models, the model 
      achieves an impressive error rate that is very similar to human 
      classification error rate. Furthermore, the authors demonstrate that the 
      model can generate realistic character drawings after receiving only one 
      training example as tested by a “Visual Turing Test” where participants 
      were unable to reliably distinguish human drawn characters from machine 
      drawn characters. The success of this approach appears to be attributed to 
      its ability to learn from the richer compositional and causal properties 
      of one image. COMMENTS TO AUTHORS: The near-human 
      level of performance in a one-shot learning task in this work is very 
      impressive and suggests that there is potentially great value in 
      exploiting compositionality and causality in this domain. My only 
      complaint is that the model is very complex which makes it a bit difficult 
      to identify where the big win is. However, the comparison to the similar 
      Affine model helps with this problem. It would be nice to see this 
      model compared directly to results presented in previous work (maybe 
      [25]). MINOR COMMENTS: I had trouble finding reference 
      [25] (R. Salakhutdinov, J. B. Tenenbaum, and A. Torralba. Hierarchical 
      deep models for one-shot learning. In Neural Information Processing 
      Systems (NIPS), 2011.) Perhaps one of these was the intended 
      reference? R. Salakhutdinov, J. Tenenbaum , A. Torralba. Learning 
      to Learn with Compound Hierarchical-Deep Models. NIPS, 2011. OR 
      R. Salakhutdinov, J. Tenenbaum, and A. Torralba . One-Shot 
      Learning with a Hierarchical Nonparametric Bayesian Model. JMLR WC&P 
      Unsupervised and Transfer Learning, 2012.