Continual Learning for Robotics
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The document describes a deep learning model that uses experience replay to improve classification performance. The model contains a data layer, output layer, and hidden layers that extract low-level and class-specific features. It performs a forward pass on native patterns and stores rehearsal patterns externally, then does a backward pass on all patterns to update weights and prevent forgetting older information.
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A number of successful Computer Vision applications have been recently proposed based on Convolutional Networks. However, in most of the cases the system is fully supervised, the training set is fixed and the task completely defined a priori. Even though Transfer Learning approaches proved to be very useful to adapt heavily pre-trained models to ever-changing scenarios, the incremental learning and adaptation capabilities of existing models is still limited and catastrophic forgetting very difficult to control. In this talk we will discuss our experience in the design of deep architectures and algorithms capable of learning objects incrementally both in a supervised and unsupervised way. Finally we will introduce a new dataset and benchmark (CORe50) that we specifically collected to focus on continuous object recognition for Robotic Vision.
Comparing Incremental Learning Strategies for Convolutional Neural Networks
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
If you are interested in this work please cite:
Lomonaco, V., & Maltoni, D. (2016, September). Comparing Incremental Learning Strategies for Convolutional Neural Networks. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 175-184). Springer International Publishing.
For further information visit my website: http://www.vincenzolomonaco.com/
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In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
A Framework for Deadlock Detection in Java
In this work we started to develop a novel framework for statically detecting deadlocks in a concurrent Java environment with asynchronous method calls and cooperative scheduling of method activations. Since this language features recursion and dynamic resource creation, dead-
lock detection is extremely complex and state-of-the-art solutions either give imprecise answers or do not scale. The basic component of the framework is a front-end inference algorithm that ex-
tracts abstract behavioral descriptions of methods, called contracts, which retain resource dependency information. This component is integrated with a back-end that analyze contracts and derive deadlock information computing a fixpoint semantics.
Deep Learning libraries and first experiments with Theano
In recent years, neural networks and deep learning techniques have shown to perform well on many
problems in image recognition, speech recognition, natural language processing and many other tasks.
As a result, a large number of libraries, toolkits and frameworks came out in different languages and
with different purposes. In this report, firstly we take a look at these projects and secondly we choose the
framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net
using this framework to test both its ease-of-use and efficiency.
Word2vec on the italian language: first experiments
Word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent years. The vector representations of words learned by word2vec models have been proven to be able to carry semantic meanings and are useful in various NLP tasks. In this work I try to reproduce the previously obtained results for the English language and to explore the possibility of doing the same for the Italian language.
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- 2. • • • • • •
- 3. • • •
- 9. • •
- 17. •
- 33. Data layer Output layer (classes) Low-level generic features Class specific discriminative features
- 34. Data layer Output layer (classes) Low-level generic features Class specific discriminative features ● ● ●
- 35. Data layer Output layer (classes) Low-level generic features Class specific discriminative features
- 36. Data layer Output layer (classes) Low-level generic features Class specific discriminative features ● ● ●
- 37. Data layer Output layer (classes) Low-level generic features Class specific discriminative features ● ● ●
- 38. ● ● ●
- 41. Data layer Output layer (classes) Low-level generic features Class specific discriminative features Backwardpass (allpatterns) Forwardpass (allpatterns) Forwardpass (nativepatterns) Concat External storage (rehearsal patterns) (at minibatch level)
- 43. 3.