Cognitively-Inspired Model for Incremental Learning Using a Few Examples

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally, while most incremental learning approaches require a large amount of training data per class. We examine the problem of incremental learning using only a few training examples, referred to as Few-Shot Incremental Learning (FSIL). To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting. We evaluate our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR- 100 for incremental and few-shot incremental learning and show that our approach achieves state-of-the-art results in terms ofclassification accuracy over all learned classes.

[1]  Alan R. Wagner,et al.  Centroid Based Concept Learning for RGB-D Indoor Scene Classification , 2019, BMVC.

[2]  Adrian Popescu,et al.  DeeSIL: Deep-Shallow Incremental Learning , 2018, ECCV Workshops.

[3]  Matthieu Guillaumin,et al.  Incremental Learning of Random Forests for Large-Scale Image Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yuxin Peng,et al.  Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification , 2014, ACM Multimedia.

[5]  R. French.  Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference , 2019, Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society.

[6]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[7]  Cordelia Schmid,et al.  Incremental Learning of Object Detectors without Catastrophic Forgetting , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Byoung-Tak Zhang,et al.  Overcoming Catastrophic Forgetting by Incremental Moment Matching , 2017, NIPS.

[9]  Matthieu Guillaumin,et al.  Incremental Learning of NCM Forests for Large-Scale Image Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Gabriela Csurka,et al.  Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Renjie Liao,et al.  Incremental Few-Shot Learning with Attention Attractor Networks , 2018, NeurIPS.

[12]  Vasant Honavar,et al.  Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[13]  Pietro Perona,et al.  The Caltech-UCSD Birds-200-2011 Dataset , 2011 .

[14]  Dahua Lin,et al.  Learning a Unified Classifier Incrementally via Rebalancing , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yoshua Bengio,et al.  An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks , 2013, ICLR.

[16]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[17]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[18]  Alexander V. Terekhov,et al.  Knowledge Transfer in Deep Block-Modular Neural Networks , 2015, Living Machines.

[19]  Robi Polikar,et al.  Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes , 2009, IEEE Transactions on Neural Networks.

[20]  Stefan Rüping,et al.  Incremental Learning with Support Vector Machines , 2001, ICDM.

[21]  G. Winocur,et al.  Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation. , 2016, Annual review of psychology.

[22]  Yandong Guo,et al.  Large Scale Incremental Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Christoph H. Lampert,et al.  Curriculum learning of multiple tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[25]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[26]  Yu-Chiang Frank Wang,et al.  A Closer Look at Few-shot Classification , 2019, ICLR.

[27]  Paul F. M. J. Verschure,et al.  Proceedings of the 4th International Conference on Biomimetic and Biohybrid Systems - Volume 9222 , 2015 .

[28]  Ronald Kemker,et al.  FearNet: Brain-Inspired Model for Incremental Learning , 2017, ICLR.

[29]  Cordelia Schmid,et al.  End-to-End Incremental Learning , 2018, ECCV.

[30]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Christoph H. Lampert,et al.  iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Robert M. French,et al.  Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting , 2004, Connect. Sci..

[33]  Bradley C. Love,et al.  Building concepts one episode at a time: The hippocampus and concept formation , 2017, Neuroscience Letters.

[34]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[35]  Matthew A. Brown,et al.  Low-Shot Learning with Imprinted Weights , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[36]  Gabriela Csurka,et al.  Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost , 2012, ECCV.

[37]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[39]  Rama Chellappa,et al.  Learning Without Memorizing , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Philip H. S. Torr,et al.  Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence , 2018, ECCV.

[41]  OctoMiao.  Overcoming catastrophic forgetting in neural networks , 2016 .

[42]  Ilja Kuzborskij,et al.  From N to N+1: Multiclass Transfer Incremental Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Louis Renoult,et al.  Autobiographically Significant Concepts: More Episodic than Semantic in Nature? An Electrophysiological Investigation of Overlapping Types of Memory , 2015, Journal of Cognitive Neuroscience.

[44]  Marcus Rohrbach,et al.  Memory Aware Synapses: Learning what (not) to forget , 2017, ECCV.

[45]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[46]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[48]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .

[49]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[50]  Nikos Komodakis,et al.  Dynamic Few-Shot Visual Learning Without Forgetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[51]  Christopher Summerfield,et al.  Comparing continual task learning in minds and machines , 2018, Proceedings of the National Academy of Sciences.

[52]  Alan Wagner,et al.  CBCL: Brain-Inspired Model for RGB-D Indoor Scene Classification , 2019 .

[53]  Derek Hoiem,et al.  Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.