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CORe50: a New Dataset and Benchmark for Continuous Object Recognition Poster

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Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while na\"ive incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.

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CORe50: a New Dataset and Benchmark for Continuous Object Recognition Poster

  1. 1. CORe50: a New Dataset and Benchmark for Continuous Object Recognition Vincenzo Lomonaco & Davide Maltoni DISI - University of Bologna, Italy Davide Maltoni and Vincenzo Lomonaco DISI - University of Bologna Emails: {vincenzo.lomonaco, davide.maltoni}@unibo.it Websites: www.vincenzolomonaco.com, http://bias.csr.unibo.it/maltoni/ Contacts [1] Maltoni Davide and Lomonaco Vincenzo. “Semi-supervised Tuning from Temporal Coherence”. 23rd International Conference on Pattern Recognition, 2016. [2] Lomonaco Vincenzo and Maltoni Davide. “Comparing Incremental Learning Strategies for Convolutional Neural Networks”. ANNPR Workshop. Springer International Publishing, 2016. References Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while naïve incremental strategies have been shown to suffer from catastrophic forgetting. In this work we propose a new Dataset and Benchmark CORe50, specifically designed for assessing Continuous Learning strategies and robotic vision applications. Biological learning does not require to store perceptual data streams and re- train the prediction model from scratch as current Deep Learning algorithms. On the contrary, it excels at processing data continuously and incrementally. With this work we aimed at providing the Deep Learning and Robotics communities with a new Dataset and Benchmark for accelerating the process of closing this gap. Through our baselines, we have shown that contrasting forgetting is possible in many ways yet leaving much room for future improvements. Check out our live website for constantly updated versions! Why CORe50? Baseline Strategies and Results Specifically Designed Dataset 3-way Benchmark Large room for improvements! https://vlomonaco.github.io/core50/ CORe50 𝐵𝑎𝑡𝑐ℎ0 𝐵𝑎𝑡𝑐ℎ1 𝐵𝑎𝑡𝑐ℎ 𝑛 . . . Initial Batch Incremental Batches  New Instances (NI) New training patterns of the same classes becomes available during time. New Classes (NC) New training patterns of different classes becomes available during time. New Instances and Classes (NIC) New training patterns of both known and unknown classes becomes available during time. Common Continuous Learning Context Three Different Scenarios of Incremental Difficulty a) b) c) Figure 1. a) The 50 different objects (categories on column) of CORe50. b) Example of 1 sec. recording at 20fps of object #26 in session #4. c) One frame of the same object (#41) throughout the 11 acquisition sessions. d) Dataset details and figures. Baseline Strategies Cumulative Training patterns are accumulated and the model is re-trained from scratch for each incremental batch. Naive Stochastic Gradient Descent (SGD) is continued on each batch. Previously encountered training data are discarded. CWR Weights are kept frozen up to conv5, fc layers removed. For each batch output units are trained and then transferred into a “consolidated” network. Results on the Three Scenarios NI, NC and NIC NI LWF Before starting training on the new batch, surrogate labels for a “multi-task” loss are computed, hence enforcing output stability among incremental batches. EWC Regularization technique based on the penalization of changes in the weights carrying higher Fisher Information computed at the end of the training of each batch. https://vlomonaco.github.io/core50/ NC NIC # Images Format Image size # Cat. # Obj. x Cat. # Session # img. x Session # Outdoor Session Acquisition Setting 164,866 RGB-D 350x350 128x128 10 5 11 ~300 3 Hand held d)

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