Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

When utilizing the deep learning models in some real applications,the distribution of the labels in the environment can be used to increase the accuracy.Generally,to compute this distribution,there should be the validation set that is labeled by the ground truths.On the other side,the dependency of ground truths limits the utilization of the distribution in various environments.In this paper,we carried out a novel system for the deep learning-based classification to solve this problem.Firstly,our system only uses one validation set with ground truths to compute some hyper parameters,which is named as one-shot guidance.Secondly,in an environment,our system builds the validation set and labels this by the prediction results,which does not need any guidance by the ground truths.Thirdly,the computed distribution of labels by the validation set selectively cooperates with the probability of labels by the output of models,which is to increase the accuracy of predict results on testing samples.We selected six popular deep learning models on three real datasets for the evaluation.The experimental results show that our system can achieve higher accuracy than state-of-art methods while reducing the dependency of labeled validation set.

Technical Summary of Rice Yield Exceeding 10 Tons per Hectare for Three Consecutive Years

In Heilongjiang Province,rice planting area increased year by year. However,due to improper cultivation methods,farmers did not have knowledge about characteristics of rice varieties,there was still no complete cultivation technology system. The yield of different regions varied widely. The increase in rice yield was relatively low,and yield per hectare remained at 7 tons. Through the recent three years of largescale demonstration,it was known that the high-yielding varieties and high-yielding cultivation methods should be promoted at the same time.In the cultivation process,it was recommended to take reliable,effective,and simple and feasible technical procedures.

Solving the Feature Diversity Problem Based on Multi-Model Scheme

Generally,the performance of deep learning models is related to the captured features of training samples.When the training samples belong to different domains,the diverse features may increase the difficulty of training high performance models.In this paper,we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification.Firstly,our framework selects some existing models and trains each of them on organized training sets to get multiple trained models.Secondly,we select some of them based on a validation set.Finally,we use some fusion method on the outputs of the selected models to get more accurate results.The experimental results show that our framework achieved higher accuracy than the existing methods.Our framework can be an option for the deep learning system to increase the classification accuracy.