Can machine learning account for human visual object shape similarity judgments?
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[1] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[2] R. Jacobs,et al. Comparing the Visual Representations and Performance of Humans and Deep Neural Networks , 2018, Current Directions in Psychological Science.
[3] Aaron R. Seitz,et al. Deep Neural Networks for Modeling Visual Perceptual Learning , 2018, The Journal of Neuroscience.
[4] Samy Bengio,et al. A Study on Overfitting in Deep Reinforcement Learning , 2018, ArXiv.
[5] Thomas L. Griffiths,et al. Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations , 2017, Cogn. Sci..
[6] François Chollet,et al. Deep Learning with Python , 2017 .
[7] Robert A Jacobs,et al. Visual Shape Perception as Bayesian Inference of 3D Object-Centered Shape Representations , 2017, Psychological review.
[8] Lucas Beyer,et al. In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.
[9] Jessica B. Hamrick,et al. psiTurk: An open-source framework for conducting replicable behavioral experiments online , 2016, Behavior research methods.
[10] Lorenzo Rosasco,et al. Generalization Properties and Implicit Regularization for Multiple Passes SGM , 2016, ICML.
[11] Jonas Kubilius,et al. Deep Neural Networks as a Computational Model for Human Shape Sensitivity , 2016, PLoS Comput. Biol..
[12] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[13] Yoram Singer,et al. Train faster, generalize better: Stability of stochastic gradient descent , 2015, ICML.
[14] Atsuto Maki,et al. Factors of Transferability for a Generic ConvNet Representation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Nikolaus Kriegeskorte,et al. Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.
[16] Robert A. Jacobs,et al. From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach , 2015, PLoS Comput. Biol..
[17] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[18] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[20] Nir Ailon,et al. Deep Metric Learning Using Triplet Network , 2014, SIMBAD.
[21] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[22] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[23] Yang Song,et al. Learning Fine-Grained Image Similarity with Deep Ranking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[24] D. Hermans,et al. Meet the Fribbles: novel stimuli for use within behavioural research , 2014, Front. Psychol..
[25] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[26] Brian Kulis,et al. Metric Learning: A Survey , 2013, Found. Trends Mach. Learn..
[27] Marc Sebban,et al. A Survey on Metric Learning for Feature Vectors and Structured Data , 2013, ArXiv.
[28] Shimon Edelman,et al. Renewing the respect for similarity , 2012, Front. Comput. Neurosci..
[29] Jürgen Schmidhuber,et al. Transfer learning for Latin and Chinese characters with Deep Neural Networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[30] Jun-Ming Xu,et al. Metric Learning for Estimating Psychological Similarities , 2012, TIST.
[31] H. Bülthoff,et al. Similarity and categorization: from vision to touch. , 2011, Acta psychologica.
[32] Christian Wallraven,et al. Categorizing natural objects: a comparison of the visual and the haptic modalities , 2011, Experimental Brain Research.
[33] H. Bülthoff,et al. Visual and haptic perceptual spaces show high similarity in humans. , 2010, Journal of vision.
[34] H. Bülthoff,et al. Multimodal similarity and categorization of novel, three-dimensional objects , 2007, Neuropsychologia.
[35] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[36] Yann LeCun,et al. Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[37] Thorsten Joachims,et al. Learning a Distance Metric from Relative Comparisons , 2003, NIPS.
[38] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[39] W. Hayward,et al. Viewpoint Dependence and Object Discriminability , 2000, Psychological science.
[40] S Edelman,et al. Representation is representation of similarities , 1996, Behavioral and Brain Sciences.
[41] M. Tarr. Visual Object Recognition: Can A Single Mechanism Suffice? , 1998 .
[42] Stephen P. Boyd,et al. Semidefinite Programming , 1996, SIAM Rev..