A general purpose intelligent surveillance system for mobile devices using deep learning

In this paper the design, implementation, and evaluation of a general purpose smartphone based intelligent surveillance system is presented. It has two main elements; i) a detection module, and ii) a classification module. The detection module is based on the recently introduced approach that combines the well-known background subtraction method with the optical flow and recursively estimated density. The classification module is based on a neural network using Deep Learning methodology. Firstly, the architecture design of the convolutional neural network is presented and analyzed in the context of the four selected architectures (two of them recent successful types) and two custom modifications specifically made for the problem at hand. The results are carefully evaluated, and the best one is selected to be used within the proposed system. In addition, the system is implemented on both a PC (using Linux type OS) and on a smartphone (using Android). In addition to the compatibility with all modern Android-based devices, most GPU powered platforms such as Raspberry Pi, Nvidia Tegra X1 and Jetson run on Linux. The proposed system can easily be installed on any such device benefiting from the advantage of parallelisation for faster execution. The proposed system achieved a performance which surpasses that of a human (classification accuracy of the top 1 class >95.9% for automatic recognition of a detected object into one of the seven selected categories. For the top-2 classes, the accuracy is even higher (99.85%). That means, at least, one of the two top classes suggested by the system is correct. Finally, a number of visual examples are showcased of the system in use in both PC and Android devices

Meta learning for supervised and unsupervised few-shot learning

Meta-learning or learning-to-learn involves automatically learning training-algorithms such that models trained with such learnt algorithms can solve a number of tasks while demonstrating high performance in a number of predefined objectives. Meta-learning has been used successfully in a large variety of application areas. In this thesis, we present three meta-learning-based methods that can very effectively tackle the problem of few-shot learning, where a model needs to be learned with only a very small amount of available training samples for each concept to be learned (e.g. 1-5 samples for each concept). Two of the proposed methods are trained using supervised learning, and another involves using self-supervised learning to achieve unsupervised meta-learning. The proposed methods build on the Model Agnostic Meta-Learning (MAML) framework. MAML is a gradient-based meta-learning method, where one learns a parameter initialization for a model, such that after the model has been updated a number of times towards a small training set (i.e. a support set), it can perform very well on previously unseen instances of the classes it was trained on, usually referred to as a small validation (i.e. a target set). The initialization is learned by utilizing two levels of learning. One inner-most level where the initialization is updated towards the support set and evaluated on the target set, thus generating an objective directly quantifying the generalization performance of the inner-loop model (i.e. the target-set loss), and an outer-most level where the parameter-initialization is learned using the gradients with respect to the target-set loss. Our first method, referred to as MAML++, improves the highly unstable MAML method using a number of modifications in the batch normalization layers, the outer loop loss formulation and the formulation of the learning scheduler used on the inner loop. Not only does MAML++ enable MAML to converge more reliably and efficiently, but it also improves the model’s generalization performance. We evaluate our method on Omniglot and Mini-ImageNet, where our model showcases vastly improved convergence speed, stability and generalization performance. The second proposed method, referred to as Self-Critique and Adapt (SCA), builds on MAML++ by allowing the inner loop model to adapt itself on the unsupervised target set, by learning an unsupervised loss function parameterized as a neural network. This unsupervised loss function is learned jointly with the parameter initialization and learning scheduler of the model as done in MAML++. SCA produces SOTA results in few-shot learning, further improving the performance of MAML++. Our model is evaluated on Omniglot and Mini-ImageNet where it sets SOTA-level performance. The third proposed method, referred to as Assume, Augment and Learn (AAL) involves sampling pseudo-supervised tasks from an unsupervised training set by leveraging random labels and data augmentation. These tasks can then be used to directly train any few-shot learning model to perform well on a given dataset. We apply our method on MAML++ and ProtoNets on two datasets, Omniglot and Mini-ImageNet where our model produces state-of-the-art (SOTA) results in Omniglot and competitive performance with SOTA methods in Mini-ImageNet

Is Scaling Learned Optimizers Worth It? Evaluating The Value of VeLO's 4000 TPU Months

We analyze VeLO (versatile learned optimizer), the largest scale attempt to train a general purpose "foundational" optimizer to date. VeLO was trained on thousands of machine learning tasks using over 4000 TPU months with the goal of producing an optimizer capable of generalizing to new problems while being hyperparameter free, and outperforming industry standards such as Adam. We independently evaluate VeLO on the MLCommons optimizer benchmark suite. We find that, contrary to initial claims: (1) VeLO has a critical hyperparameter that needs problem-specific tuning, (2) VeLO does not necessarily outperform competitors in quality of solution found, and (3) VeLO is not faster than competing optimizers at reducing the training loss. These observations call into question VeLO's generality and the value of the investment in training it

How to train your MAML

The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.Comment: Published in ICLR 201