# Metric Embedding Learning on Multi-Directional Projections
## 6 Conclusions  
In this paper the MDIPFL transformation for image dimensionality reduction was applied to compress input data of embedding learning methods. Multiple experiments were performed for instance recognition using this novel metric learning method including the Siamese architecture and triplet loss based methods. Multiple setups were examined, as the effects of multi-class pretraining and different negative selection methods were evaluated.  
Results showed, that the proposed method is efficient in object re-identification tasks. The effect of multi-class classification pretraining for training stability were verified by multiple experiments. As thecompression reduces object signature length, it also supports application in low memory systems, implementation in distributed smart camera networks is possible.  
The contribution of the paper could be extended with several future works, including the analysis of different representation-learning architectures, including NASNet [68; 69; 70], which is well-known for outperforming state-of-the-art methods for image classification tasks.  
The presented projection mapping transformation could be compared to other dimensionality reduction methods, such as classic PCA or LDA (Linear Discriminant Analysis) [71; 72; 73]. These methods should be evaluated in terms of classification accuracy as well as compression ratio and transformation runtime complexity when applied as a compression method in metric learning.  
As the performed experiments were executed to provide a proof-of-concept on projection based embedding learning, in the case of deployment in production, hyperparameter tuning [74] should be applied.  
It is worth mentioning, that the application of transfer learning seems promising, as feature extraction with pre-trained layers could result in higher overall performance with less necessary training, and novel results show [75], that high learning performance could be achieved.  
The source codes and materials are made publicly available at [[https://github.com/kerteszg/EmbeddingNet](https://github.com/kerteszg/EmbeddingNet)]([https://github.com/kerteszg/EmbeddingNet](https://github.com/kerteszg/EmbeddingNet)).  
This research received no external funding.  
The research presented in this paper was carried out as part of the EFOP-3.6.2-16-2017-00016 project in the framework of the New Szechenyi Plan. The completion of this project is funded by the European Union and co-financed by the European Social Fund. The author thanks [PERSON], [PERSON], and members of the CUDA Teaching Center at Obuda University for constructive comments and suggestions.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
AdamAdaptive Moment Estimation  
CNNConvolutional Neural Network  
EMNISTExtended MNIST  
GPUGraphics Processing Unit  
ILSVRCImageNet Large Scale Visual Recognition Challenge  
k-NNk-nearest neighbors  
MNISTModified NIST  
MDIPFLMulti-Directional Image Projections with Fixed Length  
NISTNational Institute of Standards and Technology  
PCAPrincipal Component Analysis  
RAdamRectified Adam  
ResNetResidual Network  
SNNSiamese Neural Network  
t-SNET-distributed Stochastic Neighbor Embedding  
VGGVisual Geometry Group