2 research outputs found
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning
Recent studies on automatic neural architectures search have demonstrated
significant performance, competitive to or even better than hand-crafted neural
architectures. However, most of the existing network architecture tend to use
residual, parallel structures and concatenation block between shallow and deep
features to construct a large network. This requires large amounts of memory
for storing both weights and feature maps. This is challenging for mobile and
embedded devices since they may not have enough memory to perform inference
with the designed large network model. To close this gap, we propose MemNet, an
augment-trim learning-based neural network search framework that optimizes not
only performance but also memory requirement. Specifically, it employs memory
consumption based ranking score which forces an upper bound on memory
consumption for navigating the search process. Experiment results show that, as
compared to the state-of-the-art efficient designing methods, MemNet can find
an architecture which can achieve competitive accuracy and save an average of
24.17% on the total memory needed
DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things
Internet of Medical Things (IoMT) can connect many medical imaging equipments
to the medical information network to facilitate the process of diagnosing and
treating for doctors. As medical image contains sensitive information, it is of
importance yet very challenging to safeguard the privacy or security of the
patient. In this work, a deep learning based encryption and decryption network
(DeepEDN) is proposed to fulfill the process of encrypting and decrypting the
medical image. Specifically, in DeepEDN, the Cycle-Generative Adversarial
Network (Cycle-GAN) is employed as the main learning network to transfer the
medical image from its original domain into the target domain. Target domain is
regarded as a "Hidden Factors" to guide the learning model for realizing the
encryption. The encrypted image is restored to the original (plaintext) image
through a reconstruction network to achieve an image decryption. In order to
facilitate the data mining directly from the privacy-protected environment, a
region of interest(ROI)-mining-network is proposed to extract the interested
object from the encrypted image. The proposed DeepEDN is evaluated on the chest
X-ray dataset. Extensive experimental results and security analysis show that
the proposed method can achieve a high level of security with a good
performance in efficiency