A Multiple-Loss Dual-Output Convolutional Neural Network for Fashion Class Classification

Stephen, Okeke; Maduh, Uchenna Joseph; Ibrokhimov, Sanjar; Hui, Kueh Lee; Al-Absi, Ahmed Abdulhakim; Sain, Mangal

doi:10.23919/icact.2019.8701958

Cited by 10 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To simulate GURI-S, we design CCDT-S as a 4-category classification neural network model. Specifically, we employ a multioutput classification model [38] as the CCDT-S to predict the validation result of a cancer message. Figure 2 shows the detailed structure of CCDT-S. For a given cancer message 𝑋 , we first apply a series of feature preprocessing techniques to preprocess the inputs as vector representations, which will be inputted to CCDT-S. CCDT-S is a multi-output model consisting of several independent prediction modules with identical architecture, where each module is responsible for the result code prediction of one specific rule.…”

Section: Stage 1: Ccdt-s Constructionmentioning

confidence: 99%

EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

Lu,

Xu,

Yue

et al. 2023

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

View full text Add to dashboard Cite

The Cancer Registry of Norway (CRN) collects information on cancer patients by receiving cancer messages from different medical entities (e.g., medical labs, hospitals) in Norway. Such messages are validated by an automated cancer registry system: GURI. Its correct operation is crucial since it lays the foundation for cancer research and provides critical cancer-related statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT) for GURI can facilitate various experiments and advanced analyses of the operational state of GURI without requiring intensive interactions with the real system. However, GURI constantly evolves due to novel medical diagnostics and treatment, technological advances, etc. Accordingly, CCDT should evolve as well to synchronize with GURI. A key challenge of achieving such synchronization is that evolving CCDT needs abundant data labelled by the new GURI.To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version. EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer messages from a candidate dataset and query GURI with it. We evaluate EvoCLIN-ICAL on three evolution processes. The precision, recall, and F1 score are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL. Furthermore, we replace the active learning part of EvoCLINICAL with random selection to study the contribution of transfer learning to the overall performance of EvoCLINICAL. Results show that employing active learning in EvoCLINICAL increases its performance consistently.

show abstract

Section: Stage 1: Ccdt-s Constructionmentioning

confidence: 99%

EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

Lu,

Xu,

Yue

et al. 2023

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

View full text Add to dashboard Cite

show abstract

“…Owing to the powerful feature extraction ability of the neural network, it has also generated widespread interest in remote sensing classification [6]. The convolutional neural network (CNN) can extract more abstract and invariant features in remote sensing images, and has proven its superior classification performance [7]. As a result, researchers began to focus on the development of neural networks in the field of land cover classification [8].…”

Section: Introductionmentioning

confidence: 99%

Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification

Zhang

Yan

Hu³

et al. 2022

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dualbranch convolutional neural network (CNN) classification method is designed in combination with the global information to mine the pixel features of the image. Then, the features of the two neural networks are fused with another fully neural network to realize the classification of remote sensing images. Finally, two remote sensing images are used to verify the effectiveness of our method, including hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR) imagery. The result of the proposed method is superior to the traditional single-channel convolutional neural network.

show abstract

“…The revival of deep learning methods, especially, convolutional neural networks (CNNs), provides a new perspective for FBP problem. CNN performs much better performances in plentiful computer vision tasks than traditional methods [13][14][15], such as text localization [16], image classification [17], facial landmark regression and analysis [18,19], emotion recognition [20,21], time series forecasting [22], and semantic segmentation [23]. With the adaptive feature extraction and exploration, CNN demonstrates superior capacities on the high-level computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Facial Beauty Prediction

et al. 2020

View full text Add to dashboard Cite

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

show abstract

A Multiple-Loss Dual-Output Convolutional Neural Network for Fashion Class Classification

Cited by 10 publications

References 13 publications

EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification

Deep Learning for Facial Beauty Prediction

Contact Info

Product

Resources

About