Deep Residual Shrinkage Network: Highly Noisy Data ᱞᱟᱹᱜᱤᱫ ᱢᱤᱫᱴᱟᱹᱝ Artificial Intelligence Method

Deep Residual Shrinkage Network ᱫᱚ Deep Residual Network ᱨᱮᱭᱟᱜ ᱢᱤᱫᱴᱟᱹᱝ improved variant ᱠᱟᱱᱟ ᱾ ᱢᱩᱞ ᱞᱮᱠᱟᱛᱮ ᱢᱮᱱ ᱞᱮᱠᱷᱟᱱ, Deep Residual Shrinkage Network ᱫᱚ Deep Residual Network, attention mechanisms ᱟᱨ soft thresholding functions ᱠᱚᱭ integrate-ᱟ ᱾

ᱟᱵᱚ Deep Residual Shrinkage Network ᱨᱮᱭᱟᱜ ᱠᱟᱹᱢᱤ ᱦᱚᱨᱟ (working principle) ᱱᱚᱝᱠᱟ ᱛᱮᱵᱚ ᱵᱩᱡᱷᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱯᱩᱭᱞᱩ ᱨᱮ, network ᱫᱚ attention mechanisms ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features (unimportant features) ᱠᱚᱭ ᱪᱤᱱᱦᱟᱹᱣ ᱚᱰᱚᱠᱟ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, network ᱫᱚ soft thresholding functions ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱱᱚᱣᱟ ᱠᱚ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features ᱫᱚ zero ᱨᱮᱭ ᱥᱮᱴ (set) ᱠᱟᱜᱼᱟ ᱾ ᱟᱨ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features ᱠᱚᱫᱚᱭ ᱫᱚᱦᱚ ᱠᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱯᱨᱚᱥᱮᱥ ᱫᱚ deep neural network ᱨᱮᱭᱟᱜ ᱫᱟᱲᱮᱭ ᱵᱟᱹᱲᱛᱤ ᱦᱚᱪᱚᱭᱟ ᱾ ᱱᱚᱣᱟ ᱫᱚ network ᱠᱮ noise ᱢᱮᱱᱟᱜ signals ᱠᱷᱚᱱ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features ᱚᱰᱚᱠ ᱨᱮ ᱜᱚᱲᱚᱭ ᱮᱢᱟᱜᱼᱟ ᱾

1. Research Motivation

ᱯᱩᱭᱞᱩ ᱨᱮ, ᱡᱚᱠᱷᱚᱱ algorithm ᱫᱚ samples ᱮ classify-ᱟ, ᱩᱱ ᱡᱚᱦᱚᱜ noise ᱛᱟᱦᱮᱸᱱ ᱫᱚ ᱵᱟᱝ ᱮᱲᱟᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ noise ᱨᱮᱭᱟᱜ ᱫᱟᱹᱭᱠᱟᱹ ᱠᱚᱫᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ Gaussian noise, pink noise, ᱟᱨ Laplacian noise ᱾ ᱟᱨᱦᱚᱸ ᱯᱩᱥᱴᱟᱹᱣ ᱥᱟᱺᱦᱤᱡ ᱢᱮᱱ ᱞᱮᱠᱷᱟᱱ, samples ᱨᱮ ᱟᱭᱢᱟ ᱫᱷᱟᱣ ᱱᱚᱝᱠᱟᱱ ᱠᱷᱚᱵᱚᱨ ᱛᱟᱦᱮᱸᱱᱟ ᱡᱟᱦᱟᱸ ᱫᱚ ᱱᱤᱛᱟᱜ classification task ᱞᱟᱹᱜᱤᱫ ᱡᱚᱲᱟᱣ ᱵᱟᱹᱱᱩᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱠᱚ ᱵᱟᱝ ᱡᱚᱲᱟᱣ ᱠᱷᱚᱵᱚᱨ ᱫᱚ ᱟᱵᱚ noise ᱢᱮᱱᱛᱮ ᱵᱚ ᱢᱟᱱᱟᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ noise ᱫᱚ classification performance ᱮ ᱠᱚᱢ ᱦᱚᱪᱚ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ (Soft thresholding ᱫᱚ ᱟᱭᱢᱟ signal denoising algorithms ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱟᱹᱰᱤ ᱢᱩᱞ ᱫᱷᱟᱯ ᱠᱟᱱᱟ ᱾)

ᱫᱟᱹᱭᱠᱟᱹ ᱞᱮᱠᱟᱛᱮ, ᱢᱚᱱᱮ ᱠᱟᱜ ᱢᱮ ᱦᱚᱨ ᱟᱲᱮ ᱨᱮ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ ᱾ ᱚᱸᱰᱮᱱᱟᱜ audio ᱨᱮ ᱜᱟᱹᱰᱤ ᱨᱮᱭᱟᱜ ᱥᱟᱰᱮ (car horns) ᱟᱨ ᱪᱟᱠᱟ ᱨᱮᱭᱟᱜ ᱥᱟᱰᱮ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱟᱵᱚ ᱱᱚᱣᱟ signals ᱪᱮᱛᱟᱱ ᱨᱮ speech recognition ᱵᱚ ᱠᱚᱨᱟᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱛᱟᱭᱚᱢ ᱨᱮᱭᱟᱜ ᱥᱟᱰᱮ (background sounds) ᱫᱚ results ᱮ ᱵᱟᱹᱲᱤᱡ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Deep learning ᱱᱚᱡᱚᱨ ᱛᱮ ᱧᱮᱞ ᱞᱮᱠᱷᱟᱱ, deep neural network ᱫᱚ ᱱᱚᱣᱟ horns ᱟᱨ wheels ᱥᱟᱞᱟᱜ ᱡᱚᱲᱟᱣ ᱢᱮᱱᱟᱜ features ᱠᱚ ᱚᱰᱚᱠ ᱜᱤᱰᱤ (eliminate) ᱠᱟᱜ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱱᱚᱝᱠᱟ ᱞᱮᱠᱷᱟᱱ ᱱᱚᱣᱟ features ᱫᱚ speech recognition results ᱪᱮᱛᱟᱱ ᱨᱮ ᱵᱟᱹᱲᱤᱡ ᱚᱨᱥᱚᱝ ᱵᱟᱭ ᱯᱟᱲᱟᱣ ᱦᱚᱪᱚᱭᱟ ᱾

ᱫᱚᱥᱟᱨ ᱠᱟᱛᱷᱟ, noise ᱨᱮᱭᱟᱜ ᱯᱚᱨᱤᱢᱟᱱ (amount) ᱫᱚ samples ᱵᱷᱤᱛᱨᱤ ᱨᱮ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ ᱦᱩᱭ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱵᱷᱮᱜᱟᱨ ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ dataset ᱨᱮᱦᱚᱸ ᱦᱩᱭᱩᱜ ᱜᱮᱭᱟ ᱾ (ᱱᱚᱣᱟ ᱵᱷᱮᱜᱟᱨ ᱫᱚ attention mechanisms ᱥᱟᱞᱟᱜ ᱢᱤᱞ ᱢᱮᱱᱟᱜᱼᱟ ᱾ ᱢᱤᱫᱴᱟᱹᱝ image dataset ᱨᱮᱭᱟᱜ ᱫᱟᱹᱭᱠᱟᱹ ᱵᱚ ᱦᱟᱛᱟᱣ ᱞᱮᱜᱮ ᱾ ᱜᱚᱴᱟ image ᱨᱮ target object ᱨᱮᱭᱟᱜ ᱡᱟᱭᱜᱟ ᱫᱚ ᱵᱷᱮᱜᱟᱨ ᱦᱩᱭ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Attention mechanisms ᱫᱚ ᱡᱚᱛᱚ image ᱨᱮ target object ᱨᱮᱭᱟᱜ ᱴᱷᱟᱹᱣᱠᱟᱹ ᱡᱟᱭᱜᱟ ᱨᱮ ᱫᱷᱮᱭᱟᱱ ᱮ ᱮᱢ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾)

ᱫᱟᱹᱭᱠᱟᱹ ᱞᱮᱠᱟᱛᱮ, ᱢᱤᱫᱴᱟᱹᱝ cat-and-dog classifier ᱴᱨᱮᱱᱤᱝ (training) ᱞᱟᱹᱜᱤᱫ ᱢᱚᱱᱮ ᱠᱟᱜ ᱢᱮ ᱕ ᱜᱚᱴᱟᱝ images ᱢᱮᱱᱟᱜᱼᱟ ᱡᱟᱦᱟᱸ ᱨᱮ “dog” ᱞᱮᱵᱮᱞ (label) ᱢᱮᱱᱟᱜᱼᱟ ᱾ Image 1 ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱛᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱧᱩᱛ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Image 2 ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱛᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱨᱟᱡᱽ ᱦᱟᱸᱥ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Image 3 ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱛᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱤᱢ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Image 4 ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱛᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱜᱟᱫᱷᱟ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Image 5 ᱨᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱥᱮᱛᱟ ᱟᱨ ᱢᱤᱫᱴᱟᱹᱝ ᱜᱮᱰᱮ ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Training ᱚᱠᱛᱚ ᱨᱮ, ᱱᱚᱣᱟ ᱠᱚ ᱵᱟᱝ ᱡᱚᱲᱟᱣ ᱡᱤᱱᱤᱥ (irrelevant objects) ᱫᱚ classifier ᱠᱮ ᱟᱞᱮᱭᱟ (interfere) ᱾ ᱱᱚᱣᱟ ᱡᱤᱱᱤᱥ ᱠᱚᱫᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ ᱧᱩᱛ, ᱨᱟᱡᱽ ᱦᱟᱸᱥ, ᱥᱤᱢ, ᱜᱟᱫᱷᱟ ᱟᱨ ᱜᱮᱰᱮ ᱾ ᱱᱚᱣᱟ ᱠᱷᱟᱹᱛᱤᱨ classification accuracy ᱠᱚᱢ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱡᱩᱫᱤ ᱟᱵᱚ ᱱᱚᱣᱟ ᱠᱚ ᱵᱟᱝ ᱡᱚᱲᱟᱣ ᱡᱤᱱᱤᱥ ᱵᱚ ᱪᱤᱱᱦᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ, ᱛᱚᱵᱮ ᱟᱵᱚ ᱱᱚᱣᱟ ᱠᱚ ᱥᱟᱞᱟᱜ ᱡᱚᱲᱟᱣ features ᱵᱚ ᱚᱰᱚᱠ ᱜᱤᱰᱤ (eliminate) ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱝᱠᱟ ᱛᱮ, ᱟᱵᱚ cat-and-dog classifier ᱨᱮᱭᱟᱜ accuracy ᱵᱚ ᱵᱟᱹᱲᱛᱤ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾

2. Soft Thresholding

Soft thresholding ᱫᱚ ᱟᱭᱢᱟ signal denoising algorithms ᱨᱮ core step ᱠᱟᱱᱟ ᱾ Algorithm ᱫᱚ features ᱮ eliminate-ᱟ ᱡᱩᱫᱤ ᱚᱱᱟ features ᱨᱮᱭᱟᱜ absolute values ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ threshold ᱠᱷᱚᱱ ᱠᱚᱢ ᱜᱮᱭᱟ ᱾ ᱟᱨ ᱡᱩᱫᱤ features ᱨᱮᱭᱟᱜ absolute values ᱫᱚ ᱱᱚᱣᱟ threshold ᱠᱷᱚᱱ ᱵᱟᱹᱲᱛᱤ ᱜᱮᱭᱟ, ᱛᱚᱵᱮ algorithm ᱫᱚ ᱱᱚᱣᱟ features ᱠᱚ zero ᱥᱮᱫ ᱮ shrink (ᱦᱩᱰᱤᱧ) ᱠᱟᱜᱼᱟ ᱾ Researchers ᱠᱚ ᱞᱟᱛᱟᱨ ᱨᱮ ᱮᱢ ᱟᱠᱟᱱ ᱯᱷᱚᱨᱢᱩᱞᱟ ᱛᱮ soft thresholding ᱠᱚ ᱵᱮᱵᱷᱟᱨ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ:

Input ᱞᱟᱹᱜᱤᱫ Soft thresholding output ᱨᱮᱭᱟᱜ derivative ᱫᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ:

ᱪᱮᱛᱟᱱ ᱨᱮᱭᱟᱜ ᱯᱷᱚᱨᱢᱩᱞᱟ ᱫᱚ ᱩᱫᱩᱜ ᱮᱫᱟᱭ ᱡᱮ soft thresholding ᱨᱮᱭᱟᱜ derivative ᱫᱚ 1 ᱵᱟᱝᱠᱷᱟᱱ 0 ᱦᱩᱭᱩᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱜᱩᱱ (property) ᱫᱚ ReLU activation function ᱨᱮᱭᱟᱜ ᱜᱩᱱ ᱥᱟᱞᱟᱜ ᱥᱚᱢᱟᱱ ᱜᱮᱭᱟ ᱾ ᱚᱱᱟᱛᱮ, soft thresholding ᱫᱚ deep learning algorithms ᱨᱮ gradient vanishing ᱟᱨ gradient exploding ᱨᱮᱭᱟᱜ ᱵᱚᱛᱚᱨ ᱮ ᱠᱚᱢ ᱦᱚᱪᱚᱭᱟ ᱾

Soft thresholding function ᱨᱮ, threshold ᱨᱮᱭᱟᱜ setting ᱫᱚ ᱵᱟᱨᱭᱟ ᱥᱚᱨᱛᱚ (conditions) ᱢᱟᱱᱟᱣ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱯᱩᱭᱞᱩ, threshold ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ positive number ᱦᱩᱭᱩᱜ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱫᱚᱥᱟᱨ, threshold ᱫᱚ input signal ᱨᱮᱭᱟᱜ maximum value ᱠᱷᱚᱱ ᱵᱟᱹᱲᱛᱤ ᱵᱟᱝ ᱦᱩᱭᱩᱜ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱵᱟᱝᱠᱷᱟᱱ, output ᱫᱚ ᱯᱩᱨᱟᱹ zero ᱦᱩᱭᱩᱜᱼᱟ ᱾

ᱤᱱᱟᱹ ᱥᱟᱶᱛᱮ, threshold ᱫᱚ ᱛᱮᱥᱟᱨ ᱥᱚᱨᱛᱚ ᱦᱚᱸᱭ ᱢᱟᱱᱟᱣ ᱞᱮᱠᱷᱟᱱ ᱱᱟᱯᱟᱭᱚᱜᱼᱟ ᱾ ᱡᱚᱛᱚ sample ᱨᱮᱭᱟᱜ noise content ᱞᱮᱠᱟᱛᱮ, ᱟᱯᱱᱟᱨᱟᱜ independent threshold ᱛᱟᱦᱮᱸᱱ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾

ᱚᱱᱟ ᱨᱮᱭᱟᱜ ᱠᱟᱨᱚᱱ ᱫᱚ, noise content ᱫᱚ ᱟᱭᱢᱟ ᱫᱷᱟᱣ samples ᱛᱟᱞᱟ ᱨᱮ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ ᱛᱟᱦᱮᱸᱱᱟ ᱾ ᱫᱟᱹᱭᱠᱟᱹ ᱞᱮᱠᱟᱛᱮ, ᱢᱤᱫᱴᱟᱹᱝ dataset ᱨᱮ Sample A ᱨᱮ ᱠᱚᱢ noise ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱟᱨ Sample B ᱨᱮ ᱵᱟᱹᱲᱛᱤ noise ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱝᱠᱟᱱ ᱚᱠᱛᱚ ᱨᱮ, Sample A ᱫᱚ soft thresholding ᱡᱚᱦᱚᱜ ᱦᱩᱰᱤᱧ threshold ᱮ ᱵᱮᱵᱷᱟᱨ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱟᱨ Sample B ᱫᱚ ᱢᱟᱨᱟᱝ threshold ᱮ ᱵᱮᱵᱷᱟᱨ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ Deep neural networks ᱨᱮ ᱱᱚᱣᱟ features ᱟᱨ thresholds ᱨᱮᱭᱟᱜ ᱡᱟᱦᱟᱸᱱ ᱯᱩᱥᱴᱟᱹᱣ physical definitions ᱵᱟᱹᱱᱩᱜ ᱨᱮᱦᱚᱸ, ᱢᱩᱞ ᱠᱟᱛᱷᱟ (underlying logic) ᱫᱚ ᱢᱤᱫ ᱜᱮ ᱛᱟᱦᱮᱸᱱᱟ ᱾ ᱢᱮᱱ ᱨᱮᱭᱟᱜ ᱠᱟᱛᱷᱟ ᱫᱚ, ᱡᱚᱛᱚ sample ᱞᱟᱹᱜᱤᱫ ᱢᱤᱫᱴᱟᱹᱝ independent threshold ᱛᱟᱦᱮᱸᱱ ᱞᱟᱹᱠᱛᱤ ᱠᱟᱱᱟ ᱾ ᱱᱚᱣᱟ threshold ᱫᱚ noise content ᱪᱮᱛᱟᱱ ᱨᱮ ᱱᱤᱨᱵᱷᱚᱨᱟ ᱾

3. Attention Mechanism

Computer vision ᱨᱮᱭᱟᱜ field ᱨᱮ researchers ᱠᱚᱫᱚ attention mechanisms ᱟᱹᱰᱤ ᱟᱞᱜᱟ ᱛᱮᱠᱚ ᱵᱩᱡᱷᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱡᱤᱭᱟᱹᱞᱤ ᱠᱚᱣᱟᱜ ᱧᱮᱞ ᱫᱟᱲᱮ (visual systems) ᱫᱚ ᱜᱚᱴᱟ ᱡᱟᱭᱜᱟ ᱨᱮ ᱞᱚᱜᱚᱱ ᱧᱮᱞ ᱟᱹᱪᱩᱨ ᱠᱟᱛᱮ targets ᱠᱚ ᱪᱤᱱᱦᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, ᱩᱱᱠᱩᱣᱟᱜ visual systems ᱫᱚ target object ᱪᱮᱛᱟᱱ ᱨᱮ attention ᱠᱚ ᱯᱷᱚᱠᱚᱥ (focus)-ᱟ ᱾ ᱱᱚᱣᱟ ᱠᱟᱹᱢᱤ ᱦᱚᱨᱟ ᱫᱚ systems ᱠᱚ ᱵᱟᱹᱲᱛᱤ details ᱚᱰᱚᱠ (extract) ᱨᱮ ᱜᱚᱲᱚᱭ ᱮᱢᱟᱜᱼᱟ ᱾ ᱥᱟᱶ ᱥᱟᱶᱛᱮ, systems ᱫᱚ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ ᱠᱷᱚᱵᱚᱨ (irrelevant information) ᱮ ᱫᱟᱵᱟᱣ (suppress) ᱠᱟᱜᱼᱟ ᱾ ᱵᱟᱹᱲᱛᱤ ᱵᱟᱰᱟᱭ ᱞᱟᱹᱜᱤᱫ, ᱫᱟᱭᱟᱠᱟᱛᱮ attention mechanisms ᱨᱮᱭᱟᱜ ᱯᱚᱛᱚᱵ ᱠᱚ ᱯᱟᱲᱦᱟᱣ ᱯᱮ ᱾

Squeeze-and-Excitation Network (SENet) ᱫᱚ attention mechanisms ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱢᱤᱫᱴᱟᱹᱝ ᱱᱟᱣᱟ deep learning method ᱠᱟᱱᱟ ᱾ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ samples ᱨᱮ, classification task ᱞᱟᱹᱜᱤᱫ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ feature channels ᱨᱮᱭᱟᱜ ᱮᱱᱮᱢ (contribution) ᱵᱷᱮᱜᱟᱨ ᱜᱮᱭᱟ ᱾ SENet ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱦᱩᱰᱤᱧ sub-network ᱮ ᱵᱮᱵᱷᱟᱨᱟ ᱢᱤᱫᱴᱟᱹᱝ set of weights ᱧᱟᱢ ᱞᱟᱹᱜᱤᱫ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, SENet ᱫᱚ ᱱᱚᱣᱟ weights ᱠᱚ ᱚᱱᱟ ᱨᱮᱭᱟᱜ features ᱥᱟᱞᱟᱜ ᱮ ᱜᱟᱵᱟᱬ (multiply)-ᱟ ᱾ ᱱᱚᱣᱟ ᱠᱟᱹᱢᱤ ᱫᱚ ᱡᱚᱛᱚ channel ᱨᱮ features ᱨᱮᱭᱟᱜ ᱢᱟᱨᱟᱝ-ᱦᱩᱰᱤᱧ ᱮ ᱴᱷᱤᱠᱟ ᱾ ᱟᱵᱚ ᱱᱚᱣᱟ ᱯᱨᱚᱥᱮᱥ ᱫᱚ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ feature channels ᱨᱮ ᱵᱷᱮᱜᱟᱨ ᱞᱮᱵᱮᱞ ᱨᱮᱭᱟᱜ attention ᱮᱢ ᱞᱮᱠᱟ ᱵᱚ ᱧᱮᱞ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾

ᱱᱚᱣᱟ ᱦᱚᱨᱟ (approach) ᱨᱮ, ᱡᱚᱛᱚ sample ᱨᱮᱭᱟᱜ ᱢᱤᱫᱴᱟᱹᱝ independent set of weights ᱛᱟᱦᱮᱸᱱᱟ ᱾ ᱮᱴᱟᱜ ᱞᱮᱠᱟᱛᱮ ᱢᱮᱱ ᱞᱮᱠᱷᱟᱱ, ᱡᱟᱦᱟᱸ ᱵᱟᱨᱭᱟ samples ᱨᱮᱭᱟᱜ weights ᱫᱚ ᱵᱷᱮᱜᱟᱨ ᱜᱮᱭᱟ ᱾ SENet ᱨᱮ, weights ᱧᱟᱢ ᱨᱮᱭᱟᱜ ᱦᱚᱨᱟ ᱫᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ: “Global Pooling → Fully Connected Layer → ReLU Function → Fully Connected Layer → Sigmoid Function” ᱾

4. Soft Thresholding with Deep Attention Mechanism

Deep Residual Shrinkage Network ᱫᱚ SENet sub-network ᱨᱮᱭᱟᱜ ᱜᱚᱲᱦᱚᱱ (structure) ᱮ ᱵᱮᱵᱷᱟᱨᱟ ᱾ Network ᱫᱚ ᱱᱚᱣᱟ ᱜᱚᱲᱦᱚᱱ ᱮ ᱵᱮᱵᱷᱟᱨᱟ deep attention mechanism ᱨᱮ soft thresholding ᱞᱟᱹᱜᱩ (implement) ᱞᱟᱹᱜᱤᱫ ᱾ Sub-network (ᱡᱟᱦᱟᱸ ᱫᱚ ᱟᱨᱟᱜ ᱵᱟᱠᱥᱟ ᱵᱷᱤᱛᱨᱤ ᱨᱮ ᱢᱮᱱᱟᱜᱼᱟ) ᱫᱚ Learn a set of thresholds ᱨᱮᱭᱟᱜ ᱠᱟᱹᱢᱤᱭᱟᱭ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, network ᱫᱚ ᱱᱚᱣᱟ thresholds ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱡᱚᱛᱚ feature channel ᱨᱮ soft thresholding ᱮ apply-ᱭᱟ ᱾

ᱱᱚᱣᱟ sub-network ᱨᱮ, system ᱫᱚ ᱯᱩᱭᱞᱩ input feature map ᱨᱮᱭᱟᱜ ᱡᱚᱛᱚ features ᱨᱮᱭᱟᱜ absolute values ᱮ calculate-ᱟ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, system ᱫᱚ global average pooling ᱟᱨ averaging ᱠᱚᱨᱟᱣ ᱠᱟᱛᱮ ᱢᱤᱫᱴᱟᱹᱝ feature ᱮ ᱧᱟᱢᱟ, ᱡᱟᱦᱟᱸ ᱫᱚ A ᱢᱮᱱᱛᱮ ᱵᱚ ᱩᱫᱩᱜᱟ ᱾ ᱮᱴᱟᱜ ᱦᱚᱨᱟ (path) ᱨᱮ, system ᱫᱚ global average pooling ᱛᱟᱭᱚᱢ feature map ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱦᱩᱰᱤᱧ fully connected network ᱨᱮᱭ ᱵᱚᱞᱚ ᱦᱚᱪᱚᱭᱟ ᱾ ᱱᱚᱣᱟ fully connected network ᱫᱚ Sigmoid function ᱮ ᱵᱮᱵᱷᱟᱨᱟ ᱢᱩᱪᱟᱹᱫ layer ᱞᱮᱠᱟᱛᱮ ᱾ ᱱᱚᱣᱟ function ᱫᱚ output ᱠᱮ 0 ᱟᱨ 1 ᱛᱟᱞᱟ ᱨᱮᱭ normalize-ᱟ ᱾ ᱱᱚᱣᱟ ᱯᱨᱚᱥᱮᱥ ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ coefficient ᱮ ᱮᱢᱚᱜᱼᱟ, ᱡᱟᱦᱟᱸ ᱫᱚ α ᱢᱮᱱᱛᱮ ᱵᱚ ᱩᱫᱩᱜᱟ ᱾ ᱟᱵᱚ ᱢᱩᱪᱟᱹᱫ threshold ᱫᱚ α × A ᱢᱮᱱᱛᱮ ᱵᱚ ᱩᱫᱩᱜ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱚᱱᱟᱛᱮ, threshold ᱫᱚ ᱵᱟᱨᱭᱟ ᱱᱚᱢᱵᱚᱨ ᱨᱮᱭᱟᱜ ᱜᱟᱵᱟᱬ (product) ᱠᱟᱱᱟ ᱾ ᱢᱤᱫᱴᱟᱹᱝ ᱱᱚᱢᱵᱚᱨ ᱫᱚ 0 ᱟᱨ 1 ᱛᱟᱞᱟ ᱨᱮ ᱢᱮᱱᱟᱜᱼᱟ ᱾ ᱫᱚᱥᱟᱨ ᱱᱚᱢᱵᱚᱨ ᱫᱚ feature map ᱨᱮᱭᱟᱜ absolute values ᱨᱮᱭᱟᱜ average ᱠᱟᱱᱟ ᱾ ᱱᱚᱣᱟ ᱦᱚᱨᱟ (method) ᱫᱚ threshold ᱡᱮᱢᱚᱱ positive ᱛᱟᱦᱮᱸᱱᱟ ᱚᱱᱟᱭ ᱜᱟᱨᱮᱱᱴᱤ (ensure) ᱭᱟ ᱾ ᱱᱚᱣᱟ ᱦᱚᱨᱟ ᱫᱚ threshold ᱡᱮᱢᱚᱱ ᱟᱹᱰᱤ ᱢᱟᱨᱟᱝ ᱵᱟᱝ ᱦᱩᱭᱩᱜᱼᱟ, ᱚᱱᱟ ᱦᱚᱸᱭ ᱧᱮᱞᱟ ᱾

ᱟᱨᱦᱚᱸ, ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ samples ᱫᱚ ᱵᱷᱮᱜᱟᱨ ᱵᱷᱮᱜᱟᱨ thresholds ᱮ ᱵᱮᱱᱟᱣᱟ ᱾ ᱚᱱᱟ ᱠᱷᱟᱹᱛᱤᱨ, ᱟᱵᱚ ᱱᱚᱣᱟ ᱦᱚᱨᱟ ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱵᱷᱮᱜᱟᱨ (specialized) attention mechanism ᱢᱮᱱᱛᱮ ᱵᱚ ᱵᱩᱡᱷᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ mechanism ᱫᱚ ᱱᱤᱛᱟᱜ task ᱞᱟᱹᱜᱤᱫ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features ᱮ ᱪᱤᱱᱦᱟᱹᱣᱟ ᱾ ᱱᱚᱣᱟ mechanism ᱫᱚ ᱵᱟᱨᱭᱟ convolutional layers ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱱᱚᱣᱟ features ᱠᱚ 0 ᱥᱩᱨ ᱨᱮᱭᱟᱜ values ᱨᱮᱭ ᱵᱚᱫᱚᱞᱟ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, mechanism ᱫᱚ soft thresholding ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱱᱚᱣᱟ features ᱫᱚ zero ᱨᱮᱭ ᱥᱮᱴ (set) ᱠᱟᱜᱼᱟ ᱾ ᱟᱨ ᱵᱟᱝᱠᱷᱟᱱ, mechanism ᱫᱚ ᱱᱤᱛᱟᱜ task ᱞᱟᱹᱜᱤᱫ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ features ᱮ ᱪᱤᱱᱦᱟᱹᱣᱟ ᱾ mechanism ᱫᱚ ᱵᱟᱨᱭᱟ convolutional layers ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱱᱚᱣᱟ features ᱠᱚ 0 ᱠᱷᱚᱱ ᱥᱟᱦᱟ ᱨᱮᱭᱟᱜ values ᱨᱮᱭ ᱵᱚᱫᱚᱞᱟ ᱾ ᱢᱩᱪᱟᱹᱫ ᱨᱮ, mechanism ᱫᱚ ᱱᱚᱣᱟ features ᱮ ᱫᱚᱦᱚ (preserve) ᱠᱟᱜᱼᱟ ᱾

ᱢᱩᱪᱟᱹᱫ ᱨᱮ, ᱟᱵᱚ Stack many basic modules ᱨᱮᱭᱟᱜ ᱠᱟᱹᱢᱤ ᱵᱚ ᱠᱚᱨᱟᱣᱟ ᱾ ᱟᱵᱚ convolutional layers, batch normalization, activation functions, global average pooling, ᱟᱨ fully connected output layers ᱦᱚᱸ ᱵᱚ ᱢᱮᱥᱟᱭᱟ ᱾ ᱱᱚᱣᱟ ᱯᱨᱚᱥᱮᱥ ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱯᱩᱨᱟᱹ Deep Residual Shrinkage Network ᱮ ᱵᱮᱱᱟᱣ ᱨᱟᱠᱟᱵᱟ ᱾

5. Generalization Capability

Deep Residual Shrinkage Network ᱫᱚ feature learning ᱞᱟᱹᱜᱤᱫ ᱢᱤᱫᱴᱟᱹᱝ general method ᱠᱟᱱᱟ ᱾ ᱚᱱᱟ ᱨᱮᱭᱟᱜ ᱠᱟᱨᱚᱱ ᱫᱚ, ᱟᱭᱢᱟ feature learning tasks ᱨᱮ samples ᱨᱮ ᱟᱭᱢᱟ noise ᱛᱟᱦᱮᱸᱱᱟ ᱾ Samples ᱨᱮ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ ᱠᱷᱚᱵᱚᱨ (irrelevant information) ᱦᱚᱸ ᱛᱟᱦᱮᱸᱱᱟ ᱾ ᱱᱚᱣᱟ noise ᱟᱨ ᱵᱟᱝ ᱞᱟᱹᱠᱛᱤᱭᱟᱱ ᱠᱷᱚᱵᱚᱨ ᱫᱚ feature learning ᱨᱮᱭᱟᱜ ᱠᱟᱹᱢᱤ ᱨᱮ ᱵᱟᱹᱲᱤᱡ ᱚᱨᱥᱚᱝ ᱮ ᱯᱟᱠᱟᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱫᱟᱹᱭᱠᱟᱹ ᱞᱮᱠᱟᱛᱮ:

Image classification ᱨᱮᱭᱟᱜ ᱠᱟᱛᱷᱟ ᱵᱚ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱞᱮᱜᱮ ᱾ ᱢᱤᱫᱴᱟᱹᱝ image ᱨᱮ ᱟᱭᱢᱟ ᱮᱴᱟᱜ ᱡᱤᱱᱤᱥ (objects) ᱛᱟᱦᱮᱸ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱟᱵᱚ ᱱᱚᱣᱟ ᱡᱤᱱᱤᱥ ᱠᱚᱫᱚ “noise” ᱢᱮᱱᱛᱮ ᱵᱚ ᱵᱩᱡᱷᱟᱹᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Deep Residual Shrinkage Network ᱫᱚ ᱯᱟᱞᱮ ᱱᱚᱣᱟ attention mechanism ᱮ ᱵᱮᱵᱷᱟᱨ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ Network ᱫᱚ ᱱᱚᱣᱟ “noise” ᱮ ᱧᱮᱞ ᱧᱟᱢᱟ ᱾ ᱤᱱᱟᱹ ᱛᱟᱭᱚᱢ, network ᱫᱚ soft thresholding ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱱᱚᱣᱟ “noise” ᱥᱟᱞᱟᱜ ᱡᱚᱲᱟᱣ features ᱫᱚ zero ᱨᱮᱭ ᱥᱮᱴ (set) ᱠᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ ᱠᱟᱹᱢᱤ ᱫᱚ image classification accuracy ᱵᱟᱹᱲᱛᱤ ᱨᱮ ᱜᱚᱲᱚᱭ ᱮᱢ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾

Speech recognition ᱨᱮᱭᱟᱜ ᱠᱟᱛᱷᱟ ᱵᱚ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱞᱮᱜᱮ ᱾ ᱵᱤᱥᱮᱥ ᱠᱟᱛᱮ, ᱦᱚᱨ ᱟᱲᱮ ᱨᱮ ᱵᱟᱝᱠᱷᱟᱱ factory workshop ᱵᱷᱤᱛᱨᱤ ᱨᱮ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱡᱚᱦᱚᱜ ᱟᱹᱰᱤ ᱥᱟᱰᱮ (noisy environments) ᱛᱟᱦᱮᱸᱱᱟ ᱾ Deep Residual Shrinkage Network ᱫᱚ speech recognition accuracy ᱵᱟᱹᱲᱛᱤ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱵᱟᱝᱠᱷᱟᱱ ᱠᱚᱢ ᱥᱮ ᱠᱚᱢ, network ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱦᱚᱨᱟ (methodology) ᱭ ᱩᱫᱩᱜᱟ ᱾ ᱱᱚᱣᱟ ᱦᱚᱨᱟ ᱫᱚ speech recognition accuracy ᱵᱟᱹᱲᱛᱤ ᱨᱮ ᱜᱚᱲᱚᱭ ᱮᱢ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾

Reference

Minghang Zhao, Shisheng Zhong, Xuyun Fu, Baoping Tang, Michael Pecht, Deep residual shrinkage networks for fault diagnosis, IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690.

https://ieeexplore.ieee.org/document/8850096

BibTeX

@article{Zhao2020,
  author    = {Minghang Zhao and Shisheng Zhong and Xuyun Fu and Baoping Tang and Michael Pecht},
  title     = {Deep Residual Shrinkage Networks for Fault Diagnosis},
  journal   = {IEEE Transactions on Industrial Informatics},
  year      = {2020},
  volume    = {16},
  number    = {7},
  pages     = {4681-4690},
  doi       = {10.1109/TII.2019.2943898}
}

Academic Impact

ᱱᱚᱣᱟ paper ᱫᱚ Google Scholar ᱨᱮ ᱑᱔᱐᱐ ᱠᱷᱚᱱ ᱵᱟᱹᱲᱛᱤ citations ᱮ ᱧᱟᱢ ᱟᱠᱟᱫᱟ ᱾

ᱵᱟᱝ ᱯᱩᱨᱟᱹᱣ ᱟᱠᱟᱱ ᱦᱤᱥᱟᱹᱵ (incomplete statistics) ᱞᱮᱠᱟᱛᱮ, researchers ᱠᱚᱫᱚ Deep Residual Shrinkage Network (DRSN) ᱑᱐᱐᱐ ᱠᱷᱚᱱ ᱵᱟᱹᱲᱛᱤ publications/studies ᱨᱮᱠᱚ ᱞᱟᱹᱜᱩ (applied) ᱟᱠᱟᱫᱟ ᱾ ᱱᱚᱣᱟ ᱠᱚᱫᱚ ᱟᱭᱢᱟ fields ᱨᱮ ᱢᱮᱱᱟᱜᱼᱟ ᱾ ᱱᱚᱣᱟ fields ᱠᱚᱫᱚ ᱦᱩᱭᱩᱜ ᱠᱟᱱᱟ mechanical engineering, electrical power, vision, healthcare, speech, text, radar, ᱟᱨ remote sensing ᱾