Accelerating Deep Network Training with Layer-specific Processor Allocation on Mobile Devices

2021 ◽  
Vol 27 (6) ◽  
pp. 263-272
Author(s):  
Donghee Ha ◽  
Jinse Kwon ◽  
Hyungshin Kim
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bayu Adhi Nugroho

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.


2018 ◽  
Vol 67 ◽  
pp. 132-139 ◽  
Author(s):  
Jin-Seok Park ◽  
Hyeon-Gi Kim ◽  
Do-Guk Kim ◽  
In-Jae Yu ◽  
Heung-Kyu Lee

2020 ◽  
Vol 29 (3) ◽  
pp. 1574-1595
Author(s):  
Chaleece W. Sandberg ◽  
Teresa Gray

Purpose We report on a study that replicates previous treatment studies using Abstract Semantic Associative Network Training (AbSANT), which was developed to help persons with aphasia improve their ability to retrieve abstract words, as well as thematically related concrete words. We hypothesized that previous results would be replicated; that is, when abstract words are trained using this protocol, improvement would be observed for both abstract and concrete words in the same context-category, but when concrete words are trained, no improvement for abstract words would be observed. We then frame the results of this study with the results of previous studies that used AbSANT to provide better evidence for the utility of this therapeutic technique. We also discuss proposed mechanisms of AbSANT. Method Four persons with aphasia completed one phase of concrete word training and one phase of abstract word training using the AbSANT protocol. Effect sizes were calculated for each word type for each phase. Effect sizes for this study are compared with the effect sizes from previous studies. Results As predicted, training abstract words resulted in both direct training and generalization effects, whereas training concrete words resulted in only direct training effects. The reported results are consistent across studies. Furthermore, when the data are compared across studies, there is a distinct pattern of the added benefit of training abstract words using AbSANT. Conclusion Treatment for word retrieval in aphasia is most often aimed at concrete words, despite the usefulness and pervasiveness of abstract words in everyday conversation. We show the utility of AbSANT as a means of improving not only abstract word retrieval but also concrete word retrieval and hope this evidence will help foster its application in clinical practice.


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