Deep Residual Shrinkage Network: Highly Noisy Data ꯒꯤꯗꯃꯛ Artificial Intelligence Method ꯑꯃ

Deep Residual Shrinkage Network ꯑꯁꯤ Deep Residual Network ꯀꯤ ꯍꯦꯟꯅ ꯐꯒꯠꯍꯜꯂꯕ improved variant ꯑꯃꯅꯤ꯫ ꯇꯁꯦꯡꯅ ꯍꯥꯏꯔꯕꯗ, Deep Residual Shrinkage Network ꯑꯁꯤꯅ Deep Residual Network, attention mechanisms, ꯑꯃꯗꯤ soft thresholding functions ꯁꯤꯡꯕꯨ ꯄꯨꯟꯁꯤꯟꯕꯅꯤ꯫

ꯑꯩꯈꯣꯏꯅ Deep Residual Shrinkage Network ꯀꯤ ꯊꯕꯛ ꯇꯧꯕꯒꯤ ꯃꯋꯣꯡ ꯑꯁꯤ ꯃꯈꯥꯗ ꯄꯤꯔꯤꯕ ꯃꯋꯣꯡ ꯑꯁꯤꯗ ꯈꯪꯕ ꯌꯥꯏ꯫ ꯑꯍꯥꯟꯕꯗ, network ꯑꯁꯤꯅ attention mechanisms ꯁꯤꯖꯤꯟꯅꯗꯨꯅ ꯃꯔꯨꯑꯣꯏꯗꯕ feature ꯁꯤꯡ ꯈꯪꯗꯣꯛꯏ (identify)꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, network ꯑꯁꯤꯅ soft thresholding functions ꯁꯤꯖꯤꯟꯅꯗꯨꯅ ꯃꯔꯨꯑꯣꯏꯗꯕ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ zero ꯗ ꯑꯣꯟꯊꯣꯛꯏ꯫ ꯑꯣꯟꯅꯕ ꯃꯥꯏꯀꯩꯗ, network ꯑꯁꯤꯅ ꯃꯔꯨꯑꯣꯏꯕ feature ꯁꯤꯡ ꯈꯪꯗꯣꯛꯏ ꯑꯃꯗꯤ ꯃꯔꯨꯑꯣꯏꯕ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ ꯊꯝꯏ (retains)꯫ ꯊꯧꯑꯣꯡ ꯑꯁꯤꯅ deep neural network ꯀꯤ ꯄꯥꯡꯒꯜ ꯍꯥꯡꯒꯠꯍꯜꯂꯤ꯫ ꯊꯧꯑꯣꯡ ꯑꯁꯤꯅ network ꯄꯨ noise ꯌꯥꯎꯕ signal ꯁꯤꯡꯗꯒꯤ ꯀꯥꯟꯅꯕ feature ꯁꯤꯡ ꯂꯧꯊꯣꯛꯄꯗ ꯃꯇꯦꯡ ꯄꯥꯡꯏ꯫

1. ꯊꯤꯖꯤꯟꯕꯒꯤ ꯃꯔꯝ (Research Motivation)

ꯑꯍꯥꯟꯕꯗ, algorithm ꯅ sample ꯁꯤꯡ classify ꯇꯧꯕ ꯃꯇꯝꯗ, noise ꯌꯥꯎꯕ ꯍꯥꯏꯕꯁꯤ ꯅꯥꯟꯕ ꯌꯥꯗꯕꯅꯤ꯫ Noise ꯁꯤꯡ ꯑꯁꯤꯒꯤ ꯈꯨꯗꯝꯗꯤ Gaussian noise, pink noise, ꯑꯃꯗꯤ Laplacian noise ꯅꯤ꯫ ꯍꯦꯟꯅ ꯄꯥꯛꯊꯣꯛꯅ ꯍꯥꯏꯔꯕꯗ, sample ꯁꯤꯡꯗ ꯑꯌꯥꯝꯕ ꯃꯇꯝꯗ ꯍꯧꯖꯤꯛꯀꯤ classification task ꯀ ꯃꯔꯤ ꯂꯩꯅꯗꯕ information ꯌꯥꯎꯏ꯫ ꯑꯩꯈꯣꯏꯅ ꯃꯔꯤ ꯂꯩꯅꯗꯕ information ꯁꯤꯡ ꯑꯁꯤꯕꯨ noise ꯅꯤ ꯍꯥꯏꯅ ꯂꯧꯕ ꯌꯥꯏ꯫ Noise ꯑꯁꯤꯅ classification performance ꯕꯨ ꯍꯟꯊꯍꯟꯕ ꯌꯥꯏ꯫ (Soft thresholding ꯑꯁꯤ signal denoising algorithms ꯀꯌꯥꯒꯤ ꯃꯔꯨꯑꯣꯏꯕ ꯈꯣꯡꯊꯥꯡ ꯑꯃꯅꯤ꯫)

ꯈꯨꯗꯝ ꯑꯣꯏꯅ, ꯂꯝꯕꯤ ꯃꯄꯥꯟꯗ ꯋꯥꯔꯤ ꯁꯥꯟꯅꯕ ꯑꯃ ꯈꯟꯕꯤꯌꯨ꯫ Audio ꯑꯗꯨꯗ ꯒꯥꯔꯤꯒꯤ horn ꯑꯃꯗꯤ ꯆꯥꯀꯥꯒꯤ ꯃꯈꯣꯜ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ ꯑꯩꯈꯣꯏꯅ signal ꯁꯤꯡ ꯑꯁꯤꯗ speech recognition ꯇꯧꯕ ꯌꯥꯏ꯫ Background sound ꯁꯤꯡ ꯑꯁꯤꯅ result ꯇ ꯁꯣꯏꯗꯅ ꯆꯩꯊꯦꯡ ꯄꯤꯒꯅꯤ꯫ Deep learning ꯒꯤ ꯃꯤꯠꯌꯦꯡꯗꯒꯤ ꯌꯦꯡꯕꯗ, deep neural network ꯅ horn ꯑꯃꯗꯤ ꯆꯥꯀꯥꯒ ꯃꯔꯤ ꯂꯩꯅꯕ feature ꯁꯤꯡꯕꯨ ꯂꯧꯊꯣꯛꯀꯗꯕꯅꯤ꯫ ꯃꯁꯤꯅ feature ꯁꯤꯡ ꯑꯗꯨꯅ speech recognition ꯒꯤ result ꯇ ꯑꯀꯥꯏꯕ ꯄꯤꯕꯗꯒꯤ ꯊꯤꯡꯏ꯫

ꯑꯅꯤꯁꯨꯕꯗ, sample ꯁꯤꯡꯒꯤ ꯃꯔꯛꯇ noise ꯀꯤ ꯆꯥꯡ ꯑꯁꯤ ꯑꯌꯥꯝꯕ ꯃꯇꯝꯗ ꯈꯦꯠꯅꯩ꯫ ꯈꯦꯠꯅꯕ ꯑꯁꯤ dataset ꯑꯃꯈꯛꯇꯒꯤ ꯃꯅꯨꯡꯗꯁꯨ ꯊꯣꯛꯏ꯫ (ꯈꯦꯠꯅꯕ ꯑꯁꯤꯅ attention mechanisms ꯀ ꯃꯥꯟꯅꯕ ꯂꯩ꯫ Image dataset ꯑꯃꯒꯤ ꯈꯨꯗꯝ ꯂꯧꯕꯤꯌꯨ꯫ Image ꯁꯤꯡꯒꯤ ꯃꯔꯛꯇ target object ꯀꯤ location ꯑꯁꯤ ꯈꯦꯠꯅꯕ ꯌꯥꯏ꯫ Attention mechanisms ꯅ image ꯈꯨꯗꯤꯡꯃꯛꯀꯤ target object ꯀꯤ ꯑꯀꯛꯅꯕ location ꯗ ꯃꯤꯠꯌꯦꯡ ꯊꯝꯕ (focus) ꯉꯝꯃꯤ꯫)

ꯈꯨꯗꯝ ꯑꯣꯏꯅ, “dog” ꯍꯥꯏꯅ label ꯇꯧꯔꯕ image ꯃꯉꯥ ꯂꯩꯕ cat-and-dog classifier ꯑꯃ train ꯇꯧꯔꯤ ꯍꯥꯏꯅ ꯈꯟꯂꯁꯤ꯫ Image 1 ꯗ ꯍꯨꯏ ꯑꯃ ꯑꯃꯁꯨꯡ ꯎꯆꯤ ꯑꯃ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ Image 2 ꯗ ꯍꯨꯏ ꯑꯃ ꯑꯃꯁꯨꯡ ꯒꯥꯟ ꯑꯃ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ Image 3 ꯗ ꯍꯨꯏ ꯑꯃ ꯑꯃꯁꯨꯡ ꯌꯦꯟ ꯑꯃ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ Image 4 ꯗ ꯍꯨꯏ ꯑꯃ ꯑꯃꯁꯨꯡ ꯒꯙꯥ ꯑꯃ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ Image 5 ꯗ ꯍꯨꯏ ꯑꯃ ꯑꯃꯁꯨꯡ ꯉꯥꯅꯨ ꯑꯃ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ Train ꯇꯧꯕ ꯃꯇꯝꯗ, ꯃꯔꯤ ꯂꯩꯅꯗꯕ object ꯁꯤꯡꯅ classifier ꯕꯨ ꯑꯀꯥꯏꯕ ꯄꯤꯒꯅꯤ꯫ Object ꯁꯤꯡ ꯑꯁꯤꯒꯤ ꯃꯅꯨꯡꯗ ꯎꯆꯤ, ꯒꯥꯟ, ꯌꯦꯟ, ꯒꯙꯥ, ꯑꯃꯁꯨꯡ ꯉꯥꯅꯨ ꯌꯥꯎꯔꯤ꯫ ꯑꯀꯥꯏꯕ ꯑꯁꯤꯅ classification accuracy ꯕꯨ ꯍꯟꯊꯍꯜꯂꯤ꯫ ꯀꯔꯤꯒꯨꯝꯕ ꯑꯩꯈꯣꯏꯅ ꯃꯔꯤ ꯂꯩꯅꯗꯕ object ꯁꯤꯡ ꯑꯁꯤꯕꯨ ꯈꯪꯗꯣꯛꯄ (identify) ꯉꯝꯂꯕꯗꯤ, ꯑꯩꯈꯣꯏꯅ object ꯁꯤꯡ ꯑꯁꯤꯒ ꯃꯔꯤ ꯂꯩꯅꯕ feature ꯁꯤꯡꯕꯨ ꯂꯧꯊꯣꯛꯄ ꯌꯥꯏ꯫ ꯃꯋꯣꯡ ꯑꯁꯤꯅ, ꯑꯩꯈꯣꯏꯅ cat-and-dog classifier ꯒꯤ accuracy ꯕꯨ ꯐꯒꯠꯍꯟꯕ ꯌꯥꯏ꯫

2. Soft Thresholding

Soft thresholding ꯑꯁꯤ signal denoising algorithm ꯀꯌꯥꯒꯤ ꯈ꯭ꯋꯥꯏꯗꯒꯤ ꯃꯔꯨꯑꯣꯏꯕ ꯈꯣꯡꯊꯥꯡ ꯑꯃꯅꯤ꯫ ꯀꯔꯤꯒꯨꯝꯕ feature ꯁꯤꯡꯒꯤ absolute value ꯑꯗꯨ ꯑꯀꯛꯅꯕ threshold ꯑꯃꯗꯒꯤ ꯇꯥꯊꯔꯕꯗꯤ, algorithm ꯅ feature ꯁꯤꯡ ꯑꯗꯨꯕꯨ ꯂꯧꯊꯣꯛꯏ (eliminates)꯫ ꯀꯔꯤꯒꯨꯝꯕ feature ꯁꯤꯡꯒꯤ absolute value ꯑꯗꯨ threshold ꯑꯁꯤꯗꯒꯤ ꯍꯦꯟꯂꯕꯗꯤ, algorithm ꯅ feature ꯁꯤꯡ ꯑꯗꯨꯕꯨ zero ꯒꯤ ꯃꯥꯏꯀꯩꯗ shrink ꯇꯧꯏ꯫ Researcher ꯁꯤꯡꯅ soft thresholding ꯕꯨ ꯃꯈꯥꯗ ꯄꯤꯔꯤꯕ formula ꯑꯁꯤ ꯁꯤꯖꯤꯟꯅꯗꯨꯅ ꯄꯥꯡꯊꯣꯛꯄ ꯌꯥꯏ:

Input ꯀꯤ ꯃꯇꯥꯡꯗ soft thresholding output ꯀꯤ derivative ꯑꯁꯤ:

ꯃꯊꯛꯇ ꯄꯤꯔꯤꯕ formula ꯑꯁꯤꯅ ꯇꯥꯛꯄꯗꯤ soft thresholding ꯀꯤ derivative ꯑꯁꯤ 1 ꯅꯠꯇ꯭ꯔꯒ 0 ꯅꯤ꯫ ꯃꯁꯤꯒꯤ ꯃꯒꯨꯟ ꯑꯁꯤ ReLU activation function ꯒꯤ ꯃꯒꯨꯟꯒ ꯆꯞ ꯃꯥꯟꯅꯩ꯫ ꯃꯔꯝ ꯑꯁꯤꯅ, soft thresholding ꯅ deep learning algorithm ꯁꯤꯡꯗ gradient vanishing ꯑꯃꯁꯨꯡ gradient exploding ꯒꯤ risk ꯍꯟꯊꯍꯟꯕ ꯉꯝꯃꯤ꯫

Soft thresholding function ꯑꯁꯤꯗ, threshold set ꯇꯧꯕ ꯃꯇꯝꯗ condition ꯑꯅꯤ ꯁꯨꯒꯗꯕꯅꯤ꯫ ꯑꯍꯥꯟꯕꯗ, threshold ꯑꯁꯤ positive number ꯑꯣꯏꯒꯗꯕꯅꯤ꯫ ꯑꯅꯤꯁꯨꯕꯗ, threshold ꯑꯁꯤ input signal ꯒꯤ maximum value ꯗꯒꯤ ꯍꯦꯟꯕ ꯌꯥꯔꯣꯏ꯫ ꯅꯠꯇ꯭ꯔꯕꯗꯤ, output ꯑꯗꯨ ꯄꯨꯝꯅꯃꯛ zero ꯑꯣꯏꯒꯅꯤ꯫

ꯑꯃꯁꯨꯡ, threshold ꯑꯁꯤꯅ ꯑꯍꯨꯝꯁꯨꯕ condition ꯑꯃꯁꯨ ꯏꯟꯕꯅ ꯍꯦꯟꯅ ꯐꯩ꯫ Sample ꯈꯨꯗꯤꯡꯃꯛꯀꯤ sample ꯑꯗꯨꯗ ꯌꯥꯎꯔꯤꯕ noise content ꯀꯤ ꯃꯇꯨꯡ ꯏꯟꯅ ꯃꯁꯥꯒꯤ ꯑꯣꯏꯕ independent threshold ꯂꯩꯒꯗꯕꯅꯤ꯫

ꯃꯔꯝꯗꯤ, noise content ꯑꯁꯤ sample ꯁꯤꯡꯒꯤ ꯃꯔꯛꯇ ꯑꯌꯥꯝꯕ ꯃꯇꯝꯗ ꯈꯦꯠꯅꯩ꯫ ꯈꯨꯗꯝ ꯑꯣꯏꯅ, dataset ꯑꯃꯈꯛꯇꯒꯤ ꯃꯅꯨꯡꯗ Sample A ꯗ noise ꯈꯔ ꯇꯥꯡꯅ ꯌꯥꯎꯕ ꯌꯥꯏ ꯑꯗꯨꯒ Sample B ꯗꯅ noise ꯍꯦꯟꯅ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ ꯃꯇꯝ ꯑꯁꯤꯗ, soft thresholding ꯇꯧꯕ ꯃꯇꯝꯗ Sample A ꯅ ꯄꯤꯛꯄ threshold ꯁꯤꯖꯤꯟꯅꯒꯗꯕꯅꯤ꯫ Sample B ꯅ ꯆꯥꯎꯕ threshold ꯁꯤꯖꯤꯟꯅꯒꯗꯕꯅꯤ꯫ Deep neural network ꯁꯤꯡꯗ feature ꯑꯃꯁꯨꯡ threshold ꯁꯤꯡ ꯑꯁꯤꯒꯤ ꯑꯀꯛꯅꯕ physical definition ꯂꯩꯇ꯭ꯔꯕꯁꯨ, ꯃꯈꯥꯗ ꯂꯩꯔꯤꯕ basic logic ꯑꯁꯤꯗꯤ ꯆꯞ ꯃꯥꯟꯅꯅ ꯂꯩ꯫ ꯍꯥꯏꯕꯗꯤ, sample ꯈꯨꯗꯤꯡꯃꯛꯅ independent threshold ꯑꯃ ꯂꯩꯒꯗꯕꯅꯤ꯫ ꯑꯀꯛꯅꯕ noise content ꯑꯗꯨꯅ threshold ꯑꯁꯤ ꯂꯦꯞꯄꯤ (determines)꯫

3. Attention Mechanism

Researcher ꯁꯤꯡꯅ computer vision ꯒꯤ ꯂꯝꯗ attention mechanisms ꯑꯁꯤ ꯂꯥꯏꯅ ꯈꯪꯕ ꯉꯝꯃꯤ꯫ ꯁꯥ-ꯉꯥ ꯁꯤꯡꯒꯤ visual system ꯅ area ꯄꯨꯝꯕꯕꯨ ꯊꯨꯅ scan ꯇꯧꯗꯨꯅ target ꯁꯤꯡ ꯈꯪꯗꯣꯛꯄ ꯉꯝꯃꯤ꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, visual system ꯅ target object ꯇ attention ꯊꯝꯃꯤ (focuses)꯫ ꯊꯕꯛ ꯑꯁꯤꯅ system ꯕꯨ detail ꯍꯦꯟꯅ extract ꯇꯧꯕ ꯌꯥꯍꯜꯂꯤ꯫ ꯃꯇꯝ ꯑꯁꯤꯃꯛꯇꯗ, system ꯅ ꯃꯔꯤ ꯂꯩꯅꯗꯕ information ꯕꯨ ꯅꯝꯊꯩ (suppresses)꯫ ꯑꯀꯨꯞꯄ ꯃꯔꯣꯜꯒꯤꯗꯃꯛ, ꯆꯥꯟꯕꯤꯗꯨꯅ attention mechanisms ꯒ ꯃꯔꯤ ꯂꯩꯅꯕ literature ꯌꯦꯡꯕꯤꯌꯨ꯫

Squeeze-and-Excitation Network (SENet) ꯑꯁꯤ attention mechanisms ꯁꯤꯖꯤꯟꯅꯕ ꯍꯦꯟꯅ ꯅꯧꯕ deep learning method ꯑꯃꯅꯤ꯫ Sample ꯇꯣꯉꯥꯟ-ꯇꯣꯉꯥꯟꯕꯒꯤ ꯃꯇꯨꯡ ꯏꯟꯅ, ꯇꯣꯉꯥꯟꯕ feature channel ꯁꯤꯡꯅ classification task ꯇ ꯇꯣꯉꯥꯟ-ꯇꯣꯉꯥꯟꯅ ꯃꯇꯦꯡ ꯄꯥꯡꯏ꯫ SENet ꯅ weights set ꯑꯃ ꯐꯪꯅꯕꯒꯤꯗꯃꯛ ꯑꯄꯤꯛꯄ sub-network ꯑꯃ ꯁꯤꯖꯤꯟꯅꯩ꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, SENet ꯅ weight ꯁꯤꯡ ꯑꯁꯤꯕꯨ ꯃꯔꯤ ꯂꯩꯅꯕ channel ꯁꯤꯡꯒꯤ feature ꯒ multiply ꯇꯧꯏ꯫ Operation ꯑꯁꯤꯅ channel ꯈꯨꯗꯤꯡꯃꯛꯀꯤ feature ꯒꯤ magnitude (ꯆꯥꯡ) ꯑꯗꯨ adjust ꯇꯧꯏ꯫ ꯑꯩꯈꯣꯏꯅ process ꯑꯁꯤꯕꯨ ꯇꯣꯉꯥꯟ-ꯇꯣꯉꯥꯟꯕ feature channel ꯁꯤꯡꯗ attention level ꯈꯦꯠꯅꯅ apply ꯇꯧꯕꯅꯤ ꯍꯥꯏꯅ ꯂꯧꯕ ꯌꯥꯏ꯫

Approach ꯑꯁꯤꯗ, sample ꯈꯨꯗꯤꯡꯃꯛꯅ independent weights set ꯑꯃ ꯄꯥꯏ꯫ ꯍꯥꯏꯕꯗꯤ, sample ꯑꯅꯤꯒꯤ weight ꯁꯤꯡ ꯑꯗꯨ ꯀꯩꯗꯧꯅꯨꯡꯗ ꯃꯥꯟꯅꯗꯦ꯫ SENet ꯇ, weight ꯐꯪꯅꯕꯒꯤ ꯑꯀꯛꯅꯕ path ꯗꯤ “Global Pooling → Fully Connected Layer → ReLU Function → Fully Connected Layer → Sigmoid Function” ꯅꯤ꯫

4. Deep Attention Mechanism ꯒ ꯂꯣꯏꯅꯅ Soft Thresholding

Deep Residual Shrinkage Network ꯅ SENet sub-network ꯀꯤ structure ꯁꯤꯖꯤꯟꯅꯩ꯫ Network ꯑꯁꯤꯅ structure ꯑꯁꯤ deep attention mechanism ꯒꯤ ꯃꯈꯥꯗ soft thresholding ꯄꯥꯡꯊꯣꯛꯅꯕ ꯁꯤꯖꯤꯟꯅꯩ꯫ Sub-network (red box ꯅ ꯇꯥꯛꯂꯤꯕ) ꯑꯁꯤꯅ Learn a set of thresholds ꯇꯧꯏ꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, network ꯅ threshold ꯁꯤꯡ ꯑꯁꯤ ꯁꯤꯖꯤꯟꯅꯗꯨꯅ feature channel ꯈꯨꯗꯤꯡꯃꯛꯇ soft thresholding apply ꯇꯧꯏ꯫

Sub-network ꯑꯁꯤꯗ, system ꯅ ꯑꯍꯥꯟꯕꯗ input feature map ꯀꯤ feature ꯄꯨꯝꯅꯃꯛꯀꯤ absolute value calculate ꯇꯧꯏ꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, system ꯅ global average pooling ꯑꯃꯁꯨꯡ averaging ꯇꯧꯗꯨꯅ feature ꯑꯃ ꯐꯪꯏ, ꯃꯁꯤꯕꯨ A ꯍꯥꯏꯅ ꯂꯧꯏ꯫ ꯑꯇꯣꯞꯄ path ꯑꯃꯗ, system ꯅ global average pooling ꯒꯤ ꯃꯇꯨꯡꯗ feature map ꯑꯗꯨ ꯑꯄꯤꯛꯄ fully connected network ꯑꯃꯗ input ꯇꯧꯏ꯫ Fully connected network ꯑꯁꯤꯅ Sigmoid function ꯕꯨ final layer ꯑꯣꯏꯅ ꯁꯤꯖꯤꯟꯅꯩ꯫ Function ꯑꯁꯤꯅ output ꯑꯗꯨ 0 ꯑꯃꯁꯨꯡ 1 ꯒꯤ ꯃꯔꯛꯇ normalize ꯇꯧꯏ꯫ Process ꯑꯁꯤꯅ coefficient ꯑꯃ ꯐꯪꯏ, ꯃꯁꯤꯕꯨ α ꯍꯥꯏꯅ ꯂꯧꯏ꯫ ꯑꯩꯈꯣꯏꯅ ꯑꯔꯣꯏꯕ threshold ꯑꯗꯨ α × A ꯍꯥꯏꯅ ꯐꯣꯡꯗꯣꯛꯄ ꯌꯥꯏ꯫ ꯃꯔꯝ ꯑꯁꯤꯅ, threshold ꯑꯁꯤ number ꯑꯅꯤꯒꯤ product ꯅꯤ꯫ Number ꯑꯃꯗꯤ 0 ꯑꯃꯁꯨꯡ 1 ꯒꯤ ꯃꯔꯛꯇ ꯂꯩ꯫ ꯑꯇꯣꯞꯄ number ꯑꯃꯗꯤ feature map ꯀꯤ absolute value ꯁꯤꯡꯒꯤ average ꯅꯤ꯫ Method ꯑꯁꯤꯅ threshold ꯑꯗꯨ positive ꯑꯣꯏꯍꯟꯒꯅꯤ ꯍꯥꯏꯕꯒꯤ guarantee ꯄꯤ꯫ Method ꯑꯁꯤꯅ threshold ꯑꯗꯨ ꯌꯥꯝꯅ ꯆꯥꯎꯕ ꯑꯣꯏꯍꯟꯗꯦ ꯍꯥꯏꯕꯁꯨ guarantee ꯄꯤ꯫

ꯃꯁꯤꯒꯤꯁꯨ ꯃꯊꯛꯇ, sample ꯈꯦꯠꯅꯕꯅ ꯈꯦꯠꯅꯕ threshold ꯁꯤꯡ ꯄꯨꯊꯣꯛꯏ꯫ ꯃꯔꯝ ꯑꯁꯤꯅ, ꯑꯩꯈꯣꯏꯅ method ꯑꯁꯤꯕꯨ specialized attention mechanism ꯑꯃꯅꯤ ꯍꯥꯏꯅ ꯈꯪꯕ ꯌꯥꯏ꯫ Mechanism ꯑꯁꯤꯅ ꯍꯧꯖꯤꯛꯀꯤ task ꯀ ꯃꯔꯤ ꯂꯩꯅꯗꯕ feature ꯁꯤꯡ ꯈꯪꯗꯣꯛꯏ (identifies)꯫ Mechanism ꯑꯁꯤꯅ convolutional layer ꯑꯅꯤꯒꯤ ꯈꯨꯊꯥꯡꯗ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ zero ꯒ ꯅꯛꯅꯕ value ꯗ transform ꯇꯧꯏ꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, mechanism ꯑꯁꯤꯅ soft thresholding ꯁꯤꯖꯤꯟꯅꯗꯨꯅ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ zero ꯗ set ꯇꯧꯏ꯫ ꯑꯇꯣꯞꯄ ꯃꯥꯏꯀꯩꯗ, mechanism ꯑꯁꯤꯅ ꯍꯧꯖꯤꯛꯀꯤ task ꯀ ꯃꯔꯤ ꯂꯩꯅꯕ feature ꯁꯤꯡ ꯈꯪꯗꯣꯛꯏ꯫ Mechanism ꯑꯁꯤꯅ convolutional layer ꯑꯅꯤꯒꯤ ꯈꯨꯊꯥꯡꯗ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ zero ꯗꯒꯤ ꯂꯥꯞꯅ ꯂꯩꯕ value ꯗ transform ꯇꯧꯏ꯫ ꯑꯔꯣꯏꯕꯗ, mechanism ꯑꯁꯤꯅ feature ꯁꯤꯡ ꯑꯁꯤꯕꯨ ꯊꯝꯏ (preserves)꯫

ꯑꯔꯣꯏꯕꯗ, ꯑꯩꯈꯣꯏꯅ Stack many basic modules ꯇꯧꯏ꯫ ꯑꯩꯈꯣꯏꯅ convolutional layers, batch normalization, activation functions, global average pooling, ꯑꯃꯁꯨꯡ fully connected output layers ꯁꯨ ꯌꯥꯎꯍꯜꯂꯤ꯫ Process ꯑꯁꯤꯅ ꯃꯄꯨꯡ ꯐꯥꯕ Deep Residual Shrinkage Network ꯕꯨ ꯁꯦꯝꯒꯠꯂꯤ꯫

5. ꯁꯤꯖꯤꯟꯅꯕ ꯌꯥꯕ ꯃꯐꯝ (Generalization Capability)

Deep Residual Shrinkage Network ꯑꯁꯤ feature learning ꯒꯤꯗꯃꯛ general method ꯑꯃꯅꯤ꯫ ꯃꯔꯝꯗꯤ, feature learning task ꯀꯌꯥꯗ sample ꯁꯤꯡꯗ ꯑꯌꯥꯝꯕ ꯃꯇꯝꯗ noise ꯌꯥꯎꯏ꯫ Sample ꯁꯤꯡꯗ ꯃꯔꯤ ꯂꯩꯅꯗꯕ information ꯁꯨ ꯌꯥꯎꯏ꯫ Noise ꯑꯃꯁꯨꯡ ꯃꯔꯤ ꯂꯩꯅꯗꯕ information ꯁꯤꯡ ꯑꯁꯤꯅ feature learning ꯒꯤ performance ꯗ ꯑꯀꯥꯏꯕ ꯄꯤꯕ ꯌꯥꯏ꯫ ꯈꯨꯗꯝ ꯑꯣꯏꯅ:

Image classification ꯈꯟꯕꯤꯌꯨ꯫ Image ꯑꯃꯗ ꯑꯇꯣꯞꯄ object ꯀꯌꯥ ꯑꯃ ꯄꯨꯟꯅ ꯌꯥꯎꯕ ꯌꯥꯏ꯫ ꯑꯩꯈꯣꯏꯅ object ꯁꯤꯡ ꯑꯁꯤꯕꯨ “noise” ꯅꯤ ꯍꯥꯏꯅ ꯈꯪꯕ ꯌꯥꯏ꯫ Deep Residual Shrinkage Network ꯅ attention mechanism ꯁꯤꯖꯤꯟꯅꯕ ꯉꯝꯂꯝꯕ ꯌꯥꯏ꯫ Network ꯅ “noise” ꯁꯤꯡ ꯑꯁꯤ ꯈꯪꯗꯣꯛꯏ (notices)꯫ ꯃꯗꯨꯒꯤ ꯃꯇꯨꯡꯗ, network ꯅ soft thresholding ꯁꯤꯖꯤꯟꯅꯗꯨꯅ “noise” ꯑꯁꯤꯒ ꯃꯔꯤ ꯂꯩꯅꯕ feature ꯁꯤꯡꯕꯨ zero ꯗ set ꯇꯧꯏ꯫ ꯊꯕꯛ ꯑꯁꯤꯅ image classification accuracy ꯕꯨ ꯐꯒꯠꯍꯟꯕ ꯌꯥꯏ꯫

Speech recognition ꯈꯟꯕꯤꯌꯨ꯫ ꯑꯈꯟꯅꯅ, ꯂꯝꯕꯤ ꯃꯄꯥꯟꯗ ꯅꯠꯇ꯭ꯔꯒ factory workshop ꯀꯤ ꯃꯅꯨꯡꯗ ꯋꯥꯔꯤ ꯁꯥꯟꯅꯕꯒꯨꯝꯕ noise ꯌꯥꯝꯅ ꯂꯩꯕ environment ꯁꯤꯡ ꯈꯟꯕꯤꯌꯨ꯫ Deep Residual Shrinkage Network ꯅ speech recognition accuracy ꯕꯨ ꯐꯒꯠꯍꯟꯕ ꯌꯥꯏ꯫ ꯅꯠꯇ꯭ꯔꯒ ꯌꯥꯝꯗ꯭ꯔꯕꯗ, network ꯅ methodology ꯑꯃ ꯄꯤꯔꯤ꯫ Methodology ꯑꯁꯤꯅ speech recognition accuracy ꯕꯨ ꯐꯒꯠꯍꯟꯕꯒꯤ ꯄꯥꯡꯒꯜ ꯂꯩ꯫

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

Paper ꯑꯁꯤꯅ Google Scholar ꯗ citation 1400 ꯍꯦꯟꯅ ꯐꯪꯈ꯭ꯔꯦ꯫

ꯃꯄꯨꯡ ꯐꯥꯗꯕ statistics ꯀꯤ ꯃꯇꯨꯡ ꯏꯟꯅ, researcher ꯁꯤꯡꯅ Deep Residual Shrinkage Network (DRSN) ꯑꯁꯤ publication/study 1000 ꯍꯦꯟꯕꯗ apply ꯇꯧꯈ꯭ꯔꯦ꯫ Application ꯁꯤꯡ ꯑꯁꯤꯅ ꯄꯥꯛ ꯆꯥꯎꯔꯕ ꯂꯝ (field) ꯀꯌꯥ ꯀꯣꯟꯁꯤꯜꯂꯤ꯫ ꯂꯝ ꯁꯤꯡ ꯑꯁꯤꯒꯤ ꯃꯅꯨꯡꯗ mechanical engineering, electrical power, vision, healthcare, speech, text, radar, ꯑꯃꯁꯨꯡ remote sensing ꯌꯥꯎꯔꯤ꯫