1,355 research outputs found
Sparse and Cosparse Audio Dequantization Using Convex Optimization
The paper shows the potential of sparsity-based methods in restoring
quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP
2016), we significantly extend the range of the evaluation scenarios: we
introduce the analysis (cosparse) model, we use more effective algorithms, we
experiment with another time-frequency transform. The paper shows that the
analysis-based model performs comparably to the synthesis-model, but the Gabor
transform produces better results than the originally used cosine transform.
Last but not least, we provide codes and data in a reproducible way
Mirror Descent View for Neural Network Quantization
Quantizing large Neural Networks (NN) while maintaining the performance is
highly desirable for resource-limited devices due to reduced memory and time
complexity. It is usually formulated as a constrained optimization problem and
optimized via a modified version of gradient descent. In this work, by
interpreting the continuous parameters (unconstrained) as the dual of the
quantized ones, we introduce a Mirror Descent (MD) framework for NN
quantization. Specifically, we provide conditions on the projections (i.e.,
mapping from continuous to quantized ones) which would enable us to derive
valid mirror maps and in turn the respective MD updates. Furthermore, we
present a numerically stable implementation of MD that requires storing an
additional set of auxiliary variables (unconstrained), and show that it is
strikingly analogous to the Straight Through Estimator (STE) based method which
is typically viewed as a "trick" to avoid vanishing gradients issue. Our
experiments on CIFAR-10/100, TinyImageNet, and ImageNet classification datasets
with VGG-16, ResNet-18, and MobileNetV2 architectures show that our MD variants
obtain quantized networks with state-of-the-art performance. Code is available
at https://github.com/kartikgupta-at-anu/md-bnn.Comment: This paper was accepted at AISTATS 202
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
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