Posts in category: Apple

Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model compression. DKM casts k-means clustering as an attention problem and enables joint optimization of the DNN parameters and clustering centroids. Unlike prior works that rely on additional regularizers and parameters, DKM-based compression keeps the original loss function and model…Apple Machine Learning Research

We present a framework for gesture customization requiring minimal examples from users, all without degrading the performance of existing gesture sets. To achieve this, we first deployed a large-scale study (N=500+) to collect data and train an accelerometer-gyroscope recognition model with a cross-user accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on everyday non-gesture data. Next, we design a few-shot learning framework which derives a lightweight model from our pre-trained model, enabling knowledge transfer without performance degradation. We validate our approach…Apple Machine Learning Research

In deep learning, embeddings are widely used to represent categorical entities such as words, apps, and movies. An embedding layer maps each entity to a unique vector, causing the layer’s memory requirement to be proportional to the number of entities. In the recommendation domain, a given category can have hundreds of thousands of entities, and its embedding layer can take gigabytes of memory. The scale of these networks makes them difficult to deploy in resource constrained environments, such as smartphones. In this paper, we propose a novel approach for reducing the size of an embedding…Apple Machine Learning Research

Accurately recognizing icon types in mobile applications is integral to many tasks, including accessibility improvement, UI design search, and conversational agents. Existing research focuses on recognizing the most frequent icon types, but these technologies fail when encountering an unrecognized low-frequency icon. In this paper, we work towards complete coverage of icons in the wild. After annotating a large-scale icon dataset (327,879 icons) from iPhone apps, we found a highly uneven distribution: 98 common icon types covered 92.8% of icons, while 7.2% of icons were covered by more than…Apple Machine Learning Research

All graphical user interfaces are comprised of one or more screens that may be shown to the user depending on their interactions. Identifying different screens of an app and understanding the type of changes that happen on the screens is a challenging task that can be applied in many areas including automatic app crawling, playback of app automation macros and large scale app dataset analysis. For example, an automated app crawler needs to understand if the screen it is currently viewing is the same as any previous screen that it has encountered, so it can focus its efforts on portions of the…Apple Machine Learning Research

In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural networks. We show that NFK can be computed for both supervised and unsupervised learning models, which can serve as a unified tool for representation extraction. Furthermore, we show that practical NFKs exhibit low-rank structures. We then propose an efficient algorithm that computes a low rank approximation of NFK, which scales to large datasets and networks. We show that the low-rank approximation of NFKs derived…Apple Machine Learning Research

Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defective samples, has been a long-standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state-of-the-art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks.Apple Machine Learning Research

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on…Apple Machine Learning Research

Apple Machine Learning Research

We present an efficient method for compressing a trained neural network without using any data. Our data-free method requires 14x-450x fewer FLOPs than comparable state-of-the-art methods. We break the problem of data-free network compression into a number of independent layer-wise compressions. We show how to efficiently generate layer-wise training data, and how to precondition the network to maintain accuracy during layer-wise compression. We show state-of-the-art performance on MobileNetV1 for data-free low-bit-width quantization. We also show state-of-the-art performance on data-free…Apple Machine Learning Research