216,245 research outputs found
LCNN: Lookup-based Convolutional Neural Network
Porting state of the art deep learning algorithms to resource constrained
compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose
a fast, compact, and accurate model for convolutional neural networks that
enables efficient learning and inference. We introduce LCNN, a lookup-based
convolutional neural network that encodes convolutions by few lookups to a
dictionary that is trained to cover the space of weights in CNNs. Training LCNN
involves jointly learning a dictionary and a small set of linear combinations.
The size of the dictionary naturally traces a spectrum of trade-offs between
efficiency and accuracy. Our experimental results on ImageNet challenge show
that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using
AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while
maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at
inference, but it also enables efficient training. In this paper, we show the
benefits of LCNN in few-shot learning and few-iteration learning, two crucial
aspects of on-device training of deep learning models.Comment: CVPR 1
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image
scales to capture complementary coherent characteristics. We propose a method
for learning a hierarchy of synthesis dictionaries with an image classification
goal. The dictionaries and classification parameters are trained by a
classification objective, and the sparse features are extracted by reducing a
reconstruction loss in each layer. The reconstruction objectives in some sense
regularize the classification problem and inject source signal information in
the extracted features. The performance of the proposed hierarchical method
increases by adding more layers, which consequently makes this model easier to
tune and adapt. The proposed algorithm furthermore, shows remarkably lower
fooling rate in presence of adversarial perturbation. The validation of the
proposed approach is based on its classification performance using four
benchmark datasets and is compared to a CNN of similar size
Image Super-Resolution with Deep Dictionary
Since the first success of Dong et al., the deep-learning-based approach has
become dominant in the field of single-image super-resolution. This replaces
all the handcrafted image processing steps of traditional sparse-coding-based
methods with a deep neural network. In contrast to sparse-coding-based methods,
which explicitly create high/low-resolution dictionaries, the dictionaries in
deep-learning-based methods are implicitly acquired as a nonlinear combination
of multiple convolutions. One disadvantage of deep-learning-based methods is
that their performance is degraded for images created differently from the
training dataset (out-of-domain images). We propose an end-to-end
super-resolution network with a deep dictionary (SRDD), where a high-resolution
dictionary is explicitly learned without sacrificing the advantages of deep
learning. Extensive experiments show that explicit learning of high-resolution
dictionary makes the network more robust for out-of-domain test images while
maintaining the performance of the in-domain test images.Comment: ECCV 202
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