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Integrating cameras into wireless smart rings has been challenging due to size and power constraints. We introduce IRIS, the first wireless vision-enabled smart ring system for smart home interactions. Equipped with a camera, Bluetooth radio, inertial ...

Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated ...

Existing few-shot action recognition methods have placed primary focus on improving the recognition accuracy while neglecting another important indicator in practical scenarios, i.e., model efficiency. In this paper, we make the first attempt and propose ...

Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation (i.e., the policy) for each layer, especially when using large-scale datasets such as ISLVRC-2012. This limits the practicality of ...

Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Model compression strategies, including weight ...

Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent"recognition difficulty" differences between various samples. We propose to feed different data samples with varying ...

Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, the use of diversity-based sampling is indispensable because there are many ...

Structural design of neural networks is crucial for the success of deep learning. While most prior works in evolutionary learning aim at directly searching the structure of a network, few attempts have been made on another promising track, channel ...

Memorization of training data by deep neural networks enables an adversary to mount successful membership inference attacks. Here, an adversary with blackbox query access to the model can infer whether an individual's data record was part of the model's ...

We present a novel method for neural network quantization. Our method, named UNIQ, emulates a non-uniform k-quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-...