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Compression and Decompression which Use Deep Neural Network
  • Jinxin Wei ,
  • Zhe Hou
Jinxin Wei
Vocational school at Juancheng, Vocational school at Juancheng, Vocational School at Juancheng

Corresponding Author:[email protected]

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an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. It is tested by tensorflow and mnist dataset. The abstract network is like LeNet-5. The concrete network is the inverse of the abstract network.Lossy compression can achieved by the test. The large compression ratio which is 19.6 is achieved. The decompression performance is ok through regression which treats classification as regression.