InceptionTime: Finding AlexNet for time series classification

Hassan Ismail Fawaz1, Benjamín Lucas2, Germain Forestier1, Charlotte Pelletier2,3, Daniel F. Schmidt2, Jonathan Weber1, Geoff Webb2, Lhassane Idoumghar1, Pierre Alain Muller1, François Petitjean2
1Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499
2Monash University [Melbourne]
3Observation de l’environnement par imagerie complexe

Tóm tắt

Từ khóa


Tài liệu tham khảo

Bagnall A, Lines J, Hills J, Bostrom A (2016) Time-series classification with COTE: the collective of transformation-based ensembles. In: International conference on data engineering, pp 1548–1549

Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31(3):606–660

Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? Mach Learn Res 17(1):152–161

Brunel A, Pasquet J, Pasquet J, Rodriguez N, Comby F, Fouchez D, Chaumont M (2019) A CNN adapted to time series for the classification of Supernovae. In: Electronic imaging

Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. ArXiv:1603.06995

Cuturi M, Blondel M (2017) Soft-dtw: a differentiable loss function for time-series. In: International conference on machine learning, pp 894–903

Dau HA, Bagnall A, Kamgar K, Yeh CCM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2018) The ucr time series archive. ArXiv

Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. Mach Learn Res 7:1–30

Forestier G, Petitjean F, Senin P, Despinoy F, Huaulmé A, Ismail Fawaz H, Weber J, Idoumghar L, Muller PA, Jannin P (2018) Surgical motion analysis using discriminative interpretable patterns. Artif Intell Med 91:3–11

Friedman M (1940) A comparison of alternative tests of significance for the problem of $$m$$ rankings. Ann Math Stat 11(1):86–92

Garcia S, Herrera F (2008) An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. Mach Learn Res 9:2677–2694

Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feed forward neural networks. In: International conference on artificial intelligence and statistics vol 9, pp 249–256

Guan C, Wang X, Zhang Q, Chen R, He D, Xie X (2019) Towards a deep and unified understanding of deep neural models in NLP. In: International conference on machine learning, pp 2454–2463

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778

Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Disc 28(4):851–881

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 4700–4708

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Transfer learning for time series classification. In: IEEE international conference on big data, pp 1367–1376

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019a) Adversarial attacks on deep neural networks for time series classification. In: IEEE international joint conference on neural networks

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019b) Deep learning for time series classification: a review. Data Min Knowl Discov

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019c) Deep neural network ensembles for time series classification. In: IEEE international joint conference on neural networks

Ismail Fawaz H, Forestier G, Weber J, Petitjean F, Idoumghar L, Muller PA (2019d) Automatic alignment of surgical videos using kinematic data. In: Artificial intelligence in medicine, pp 104–113

Karimi-Bidhendi S, Munshi F, Munshi A (2018) Scalable classification of univariate and multivariate time series. In: IEEE international conference on big data, pp 1598–1605

Kashiparekh K, Narwariya J, Malhotra P, Vig L, Shroff G (2019) Convtimenet: A pre-trained deep convolutional neural network for time series classification. In: IEEE international joint conference on neural networks

Keogh EJ, Pazzani MJ (2001) Derivative dynamic time warping. In: Proceedings of the 2001 SIAM international conference on data mining, SIAM, pp 1–11

Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: International conference on learning representations

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

Le Guennec A, Malinowski S, Tavenard R (2016) Data augmentation for time series classification using convolution neural networks. In: ECML/PKDD workshop on advanced analytics and learning on temporal data

LeCun Y, Bottou L, Orr GB, Müller KR (1998) Efficient backprop. In: Neural networks: tricks of the trade, this book is an outgrowth of a 1996 NIPS workshop, pp 9–50

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

Lee W, Park S, Joo W, Moon IC (2018) Diagnosis prediction via medical context attention networks using deep generative modeling. In: IEEE international conference on data mining, pp 1104–1109

Lines J, Bagnall A (2015) Time series classification with ensembles of elastic distance measures. Data Min Knowl Disc 29(3):565–592

Lines J, Taylor S, Bagnall A (2016) HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification. In: IEEE international conference on data mining, pp 1041–1046

Liu Y, Yu J, Han Y (2018) Understanding the effective receptive field in semantic image segmentation. Multimed Tools Appl 77(17):22159–22171

Lucas B, Shifaz A, Pelletier C, O’Neill L, Zaidi N, Goethals B, Petitjean F, Webb GI (2019) Proximity forest: an effective and scalable distance-based classifier for time series. Data Min Knowl Disc 33(3):607–635

Luo W, Li Y, Urtasun R, Zemel R (2016) Understanding the effective receptive field in deep convolutional neural networks. In: Advances in neural information processing systems, pp 4898–4906

Marteau P (2009) Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans Pattern Anal Mach Intell 31(2):306–318

Pelletier C, Webb GI, Petitjean F (2019) Temporal convolutional neural network for the classification of satellite image time series. Remote Sens 11(5):523

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

Scardapane S, Wang D (2017) Randomness in neural networks: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 7(2):e1200

Schäfer P (2015a) The boss is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29(6):1505–1530

Schäfer P (2015b) Scalable time series classification. Data Min Knowl Discov, pp 1–26

Schäfer P, Leser U (2017) Fast and accurate time series classification with WEASEL. In: Proceedings of the 2017 ACM on conference on information and knowledge management, ACM, pp 637–646

Stefan A, Athitsos V, Das G (2013) The move-split-merge metric for time series. IEEE Trans Knowl Data Eng 25(6):1425–1438

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI conference on artificial intelligence

Tan CW, Webb GI, Petitjean F (2017) Indexing and classifying gigabytes of time series under time warping. In: Proceedings of the 2017 SIAM international conference on data mining, SIAM, pp 282–290

Vlachos M, Hadjieleftheriou M, Gunopulos D, Keogh E (2006) Indexing multidimensional time-series. VLDB J Int J Very Large Data Bases 15(1):1–20

Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: A strong baseline. In: International joint conference on neural networks, pp 1578–1585

Yi F, Yu Z, Zhuang F, Zhang X, Xiong H (2018) An integrated model for crime prediction using temporal and spatial factors. In: IEEE international conference on data mining, pp 1386–1391

Yuan Y, Xun G, Ma F, Wang Y, Du N, Jia K, Su L, Zhang A (2018) Muvan: A multi-view attention network for multivariate temporal data. In: IEEE international conference on data mining, pp 717–726

Zhang C, Tavanapong W, Kijkul G, Wong J, de Groen PC, Oh J (2018) Similarity-based active learning for image classification under class imbalance. In: IEEE international conference on data mining, pp 1422–1427