scholarly journals Towards Synthetic AI Training Data for Image Classification in Intralogistic Settings

2022 ◽  
pp. 325-336
Author(s):  
Daniel Schoepflin ◽  
Karthik Iyer ◽  
Martin Gomse ◽  
Thorsten Schüppstuhl
Keyword(s):  
Domain Knowledge ◽  
Case Analysis ◽  
Training Data ◽  
Use Case ◽  
Data Generation ◽  
Promising Alternative ◽  
Proper Training ◽  
Semantic Scene ◽  
Universal Nature ◽  
Synthetic Training Data

Abstract Obtaining annotated data for proper training of AI image classifiers remains a challenge for successful deployment in industrial settings. As a promising alternative to handcrafted annotations, synthetic training data generation has grown in popularity. However, in most cases the pipelines used to generate this data are not of universal nature and have to be redesigned for different domain applications. This requires a detailed formulation of the domain through a semantic scene grammar. We aim to present such a grammar that is based on domain knowledge for the production-supplying transport of components in intralogistic settings. We present a use-case analysis for the domain of production supplying logistics and derive a scene grammar, which can be used to formulate similar problem statements in the domain for the purpose of data generation. We demonstrate the use of this grammar to feed a scene generation pipeline and obtain training data for an AI based image classifier.

scholarly journals Synthetic Training Data Generation for Visual Object Identification on Load Carriers

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1257-1262
Author(s):  
Daniel Schoepflin ◽  
Dirk Holst ◽  
Martin Gomse ◽  
Thorsten Schüppstuhl
Keyword(s):  
Object Identification ◽  
Training Data ◽  
Visual Object ◽  
Data Generation ◽  
Visual Object Identification ◽  
Synthetic Training Data

scholarly journals Hierarchy-based semantic embeddings for single-valued & multi-valued categorical variables

2021 ◽  
Author(s):  
Summaya Mumtaz ◽  
Martin Giese
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
Domain Knowledge ◽  
Similarity Measures ◽  
Training Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Use Case ◽  
Data Types ◽  
Semantic Similarity Measure

AbstractIn low-resource domains, it is challenging to achieve good performance using existing machine learning methods due to a lack of training data and mixed data types (numeric and categorical). In particular, categorical variables with high cardinality pose a challenge to machine learning tasks such as classification and regression because training requires sufficiently many data points for the possible values of each variable. Since interpolation is not possible, nothing can be learned for values not seen in the training set. This paper presents a method that uses prior knowledge of the application domain to support machine learning in cases with insufficient data. We propose to address this challenge by using embeddings for categorical variables that are based on an explicit representation of domain knowledge (KR), namely a hierarchy of concepts. Our approach is to 1. define a semantic similarity measure between categories, based on the hierarchy—we propose a purely hierarchy-based measure, but other similarity measures from the literature can be used—and 2. use that similarity measure to define a modified one-hot encoding. We propose two embedding schemes for single-valued and multi-valued categorical data. We perform experiments on three different use cases. We first compare existing similarity approaches with our approach on a word pair similarity use case. This is followed by creating word embeddings using different similarity approaches. A comparison with existing methods such as Google, Word2Vec and GloVe embeddings on several benchmarks shows better performance on concept categorisation tasks when using knowledge-based embeddings. The third use case uses a medical dataset to compare the performance of semantic-based embeddings and standard binary encodings. Significant improvement in performance of the downstream classification tasks is achieved by using semantic information.

scholarly journals A three-dimensional virtual mouse generates synthetic training data for behavioral analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 378-381 ◽  
Author(s):  
Luis A. Bolaños ◽  
Dongsheng Xiao ◽  
Nancy L. Ford ◽  
Jeff M. LeDue ◽  
Pankaj K. Gupta ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Training Data ◽  
Behavioral Analysis ◽  
Virtual Mouse ◽  
Synthetic Training Data

scholarly journals Yolo V4 for advanced Traffic Sign Recognition with synthetic training data generated by various GAN

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Yan-Ting Liu ◽  
Xiaoyi Jiang ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Training Data ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Synthetic Training Data
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