2,735 research outputs found
Deep Multiple Description Coding by Learning Scalar Quantization
In this paper, we propose a deep multiple description coding framework, whose
quantizers are adaptively learned via the minimization of multiple description
compressive loss. Firstly, our framework is built upon auto-encoder networks,
which have multiple description multi-scale dilated encoder network and
multiple description decoder networks. Secondly, two entropy estimation
networks are learned to estimate the informative amounts of the quantized
tensors, which can further supervise the learning of multiple description
encoder network to represent the input image delicately. Thirdly, a pair of
scalar quantizers accompanied by two importance-indicator maps is automatically
learned in an end-to-end self-supervised way. Finally, multiple description
structural dissimilarity distance loss is imposed on multiple description
decoded images in pixel domain for diversified multiple description generations
rather than on feature tensors in feature domain, in addition to multiple
description reconstruction loss. Through testing on two commonly used datasets,
it is verified that our method is beyond several state-of-the-art multiple
description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing
datasets for "Deep Optimized Multiple Description Image Coding via Scalar
Quantization Learning" can be found in the website of
https://github.com/mdcnn/Deep-Multiple-Description-Codin
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
Deep neural networks (DNN) have shown remarkable success in a variety of
machine learning applications. The capacity of these models (i.e., number of
parameters), endows them with expressive power and allows them to reach the
desired performance. In recent years, there is an increasing interest in
deploying DNNs to resource-constrained devices (i.e., mobile devices) with
limited energy, memory, and computational budget. To address this problem, we
propose Entropy-Constrained Trained Ternarization (EC2T), a general framework
to create sparse and ternary neural networks which are efficient in terms of
storage (e.g., at most two binary-masks and two full-precision values are
required to save a weight matrix) and computation (e.g., MAC operations are
reduced to a few accumulations plus two multiplications). This approach
consists of two steps. First, a super-network is created by scaling the
dimensions of a pre-trained model (i.e., its width and depth). Subsequently,
this super-network is simultaneously pruned (using an entropy constraint) and
quantized (that is, ternary values are assigned layer-wise) in a training
process, resulting in a sparse and ternary network representation. We validate
the proposed approach in CIFAR-10, CIFAR-100, and ImageNet datasets, showing
its effectiveness in image classification tasks.Comment: Proceedings of the CVPR'20 Joint Workshop on Efficient Deep Learning
in Computer Vision. Code is available at
https://github.com/d-becking/efficientCNN
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