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Hash Coding

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jiaoxu miao

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Hash Coding

  1. 1. Deep Hashing Network Presented by Miao Jiaxu
  2. 2. Brief Introduction Image big data with large volume and high dimension. Retrieval of images with both computation efficiency and search quality. Hashing Method: transform high-dimensional data into compact binary codes and generate similar binary codes for similar data items.
  3. 3. Brief Introduction Hashing Method Data Independent Data Dependent Unsupervised Method Supervised Method http://cs.nju.edu.cn/lwj/L2H.html#unsupervised-hashing
  4. 4. Deep Hashing Network •Motivation • uncontrollable quantization error • large approximation error by adopting ordinary distance between continuous embeddings as the surrogate of Hamming distance between binary codes. Deep Hashing Network for Efficient Similarity Retrieval AAAI2016
  5. 5. Deep Hashing Network Deep Hashing Network for Efficient Similarity Retrieval AAAI2016 (1) a sub-network with multiple convolution-pooling layers to capture image representations; (2) a fully-connected hashing layer to generate compact binary hash codes; (3) a pairwise cross-entropy loss layer for similarity-preserving learning; (4) a pairwise quantization loss for controlling hashing quality.
  6. 6. Deep Hashing Network • Bayesian Framework • fch output [-1,1] : tanh() • logarithm Maximum a Posteriori (MAP) estimation • Deep Hashing Network for Efficient Similarity Retrieval AAAI2016
  7. 7. Deep Hashing Network • logarithm Maximum a Posteriori (MAP) estimation • Deep Hashing Network for Efficient Similarity Retrieval AAAI2016
  8. 8. Deep Hashing Network • Deep Hashing Network for Efficient Similarity Retrieval AAAI2016 |x|=log(cosh(x))
  9. 9. Deep Hashing Network • Result Deep Hashing Network for Efficient Similarity Retrieval AAAI2016
  10. 10. Deep Hashing Network • Result Deep Hashing Network for Efficient Similarity Retrieval AAAI2016
  11. 11. Deep Hashing Network • Motivation Adversarial Binary Coding for Efficient Person Re-identification
  12. 12. Deep Hashing Network Adversarial Binary Coding for Efficient Person Re-identification Adversarial Binary Coding
  13. 13. Deep Hashing Network • Details • Feature layer: ReLU [0,1] • The features extracted by the network will be very close to 0 since they share the same scale with weights. On the contrary, our ABC expects every dimension of the output features to be constrained near 0 or 1. As a consequence, we will encounter an unstable optimization process if not adopting any normalization. Adversarial Binary Coding for Efficient Person Re-identification Unstable
  14. 14. Deep Hashing Network Adversarial Binary Coding for Efficient Person Re-identification Adversarial Binary Coding

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