An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
Abstract
:1. Introduction
2. Related Works
2.1. CBRSIR Based on Deep Learning
2.2. Noise Robust Loss Functions
2.3. Multilayer Perceptron for Remote Sensing
3. Methodology
3.1. The Active Passive Loss (APL)
3.2. Framework of Our Method
3.3. Two Metrics
3.4. Adaptive Weighted Learning Network
Algorithm 1: Training Process of AWNet and classifier |
Input: Data: training dataset with noisy labels Dn; Component: classifier f(·) and AWNet w(·); Parameter: α, β pretraining epochs tp, max epochs tmax and iteration per-epoch e; |
Output: Well-trained f(·) and w(·). |
1. i = 1; 2. while i < tmax + 1 do: 3. if i < tp + 1, then: 4. j = 1, α = 1, β = 1; 5. while j < e + 1 do: 6. Train the classifier f(·) by Dn; 7. Update f(·) according to Equation (1); 8. j = j + 1 9. end while 10. else: 11. k = 1 12. while k < e + 1 do: 13. Train the classifier f(·) by Dn; 14. Calculate RES by Equation (4) and δ by Equation (5); 15. Get α and β by Equation (6) 16. Update f(·) according to (1); 17. Train the classifier f(·) by Dn; 18. Update w(·) according to (1); 19. k = k + 1 20. end while 21. i = i + 1 22. end while |
4. Experiments and Analysis
4.1. Datasets and Experimental Setup
4.2. Experiments on Adaptive Weights versus Manual Weights
4.3. Comparison with Various SOTA Losses
- (1)
- (2)
- (3)
- RCE [15]: It can be seen as the reverse version of the CE, as it exchanges the positions of the predicted probability and the one-hot coding in the formula of the cross-entropy loss function. However, it also converges slowly.
- (4)
- SCE [15]: It combines the CE loss with the RCE. Its robustness and convergence stability are guaranteed by RCE and CE, respectively. However, it requires the adjustment of two hyperparameters.
- (5)
- ACE [16]: It uses the predicted probabilities pt of the true labels of the samples to adaptively de-termine the two weights in SCE. As the pt of the sample tends toward zero, it gradually transforms into RCE.
- (6)
- (7)
- AEL [31]: It is an asymmetric noise-robust function, which assumes that the noise distribution in the data satisfies the clean label domination assumption.
4.4. Efficiency and Backbone Analysis
4.5. Ablation Experiment of Two Metrics
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Loss | The Noise Rate of UCMD | ||||
---|---|---|---|---|---|
Clean (0%) | 5.0% | 10.0% | 20.0% | 30% | |
CE | 97.09 ± 0.37 | 94.37 ± 0.02 | 89.81 ± 0.40 | 78.89 ± 0.87 | 74.58 ± 2.99 |
0.1 NCE + 0.1 RCE | 95.82 ± 0.24 | 95.49 ± 0.85 | 94.16 ± 0.64 | 90.81 ± 0.66 | 84.91 ± 1.45 |
0.1 NCE + 1 RCE | 96.63 ± 0.24 | 95.12 ± 1.02 | 93.59 ± 1.00 | 91.01 ± 0.66 | 85.79 ± 0.91 |
0.1 NCE + 10 RCE | 96.64 ± 0.48 | 95.01 ± 0.74 | 93.54 ± 0.15 | 92.33 ± 0.67 | 87.48 ± 0.52 |
0.1 NCE + 100 RCE | 96.66 ± 0.39 | 94.01 ± 0.73 2 | 94.13 ± 0.96 | 89.91 ± 0.67 2 | 84.14 ± 1.04 2 |
1 NCE + 0.1 RCE | 95.44 ± 0.50 2 | 95.46 ± 0.66 | 94.61 ± 0.60 | 92.56 ± 0.31 1 | 88.25 ± 0.74 |
1 NCE + 1 RCE | 95.95 ± 0.37 | 95.63 ± 0.17 | 94.18 ± 0.39 | 90.12 ± 0.50 | 86.17 ± 0.90 |
1 NCE + 10 RCE | 96.64 ± 0.49 | 95.58 ± 0.39 | 94.50 ± 0.84 | 91.67 ± 0.93 | 86.39 ± 0.59 |
1 NCE + 100 RCE | 96.80 ± 0.45 | 95.33 ± 0.98 | 94.56 ± 0.45 | 91.74 ± 0.29 | 87.90 ± 0.68 |
10 NCE + 0.1 RCE | 96.77 ± 0.70 | 95.74 ± 0.29 1 | 93.28 ± 0.71 2 | 91.10 ± 1.67 | 89.19 ± 0.67 1 |
10 NCE + 1 RCE | 96.82 ± 0.59 1 | 95.65 ± 0.42 | 94.79 ± 0.71 1 | 92.43 ± 1.26 | 89.15 ± 0.68 |
10 NCE + 10 RCE | 96.54 ± 0.57 | 95.73 ± 0.93 | 94.63 ± 0.79 | 91.23 ± 1.19 | 87.42 ± 1.69 |
10 NCE + 100 RCE | 96.47 ± 0.57 | 94.78 ± 0.61 | 94.33 ± 0.72 | 91.28 ± 1.45 | 86.44 ± 0.99 |
A-NCE + RCE (ours) | 97.00 ± 0.87 * | 96.51 ± 0.40 * | 95.02 ± 0.51 * | 93.00 ± 0.32 * | 90.01 ± 0.88 * |
Loss | The Noise Rate of AID | ||||
---|---|---|---|---|---|
Clean (0%) | 5.0% | 10.0% | 20.0% | 30% | |
CE | 93.17 ± 0.94 | 90.07 ± 0.39 | 84.15 ± 0.46 | 71.95 ± 0.15 | 69.45 ± 3.41 |
0.1 NCE + 0.1 RCE | 92.58 ± 0.62 | 92.06 ± 0.40 | 90.90 ± 0.48 | 88.38 ± 0.18 | 85.99 ± 0.61 |
0.1 NCE + 1 RCE | 92.89 ± 0.48 | 92.13 ± 0.27 | 91.30 ± 0.22 1 | 88.06 ± 0.47 | 85.71 ± 0.32 |
0.1 NCE + 10 RCE | 92.34 ± 0.44 | 91.69 ± 0.22 | 90.82 ± 0.28 | 88.63 ± 0.30 | 84.79 ± 0.82 |
0.1 NCE + 100 RCE | 92.93 ± 0.42 | 91.91 ± 0.21 | 89.80 ± 0.46 2 | 88.34 ± 0.63 | 84.28 ± 0.20 2 |
1 NCE + 0.1 RCE | 92.55 ± 0.27 | 92.43 ± 0.11 1 | 91.01 ± 0.44 | 89.74 ± 0.43 | 86.94 ± 0.37 1 |
1 NCE + 1 RCE | 92.39 ± 0.32 | 91.98 ± 0.48 | 91.05 ± 0.53 | 89.37 ± 0.36 | 86.53 ± 0.11 |
1 NCE + 10 RCE | 91.95 ± 0.30 2 | 92.36 ± 0.32 | 91.25 ± 0.33 | 89.06 ± 0.75 | 85.50 ± 0.32 |
1 NCE + 100 RCE | 92.60 ± 0.26 | 92.38 ± 0.57 | 90.93 ± 0.54 | 87.85 ± 0.88 2 | 85.51 ± 0.63 |
10 NCE + 0.1 RCE | 93.25 ± 0.17 1 | 92.41 ± 0.75 | 90.26 ± 0.30 | 89.30 ± 0.99 | 86.78 ± 1.57 |
10 NCE + 1 RCE | 92.76 ± 0.31 | 92.27 ± 0.12 | 90.89 ± 0.13 | 89.79 ± 0.68 1 | 86.53 ± 0.35 |
10 NCE + 10 RCE | 92.94 ± 0.32 | 92.40 ± 0.11 | 91.08 ± 0.12 | 89.66 ± 0.30 | 86.63 ± 0.30 |
10 NCE + 100 RCE | 92.89 ± 0.33 | 91.58 ± 0.13 2 | 91.15 ± 0.35 | 88.48 ± 0.40 | 85.14 ± 0.98 |
A-NCE + RCE (ours) | 93.68 ± 0.38 * | 92.74 ± 0.05 * | 92.37 ± 0.22 * | 90.22 ± 0.70 * | 87.17 ± 0.54 * |
Loss | The Noise Rate of NWPU | ||||
---|---|---|---|---|---|
Clean (0%) | 5.0% | 10.0% | 20.0% | 30% | |
CE | 90.99 ± 0.75 | 85.96 ± 0.82 | 81.57 ± 1.84 | 80.41 ± 0.52 | 77.07 ± 0.03 |
0.1 NCE + 0.1 RCE | 89.55 ± 0.42 | 89.47 ± 0.53 | 87.92 ± 0.63 | 86.41 ± 0.26 | 84.15 ± 0.26 |
0.1 NCE + 1 RCE | 89.33 ± 0.28 | 89.47 ± 0.12 | 88.04 ± 0.54 | 86.99 ± 0.42 | 84.16 ± 0.59 |
0.1 NCE + 10 RCE | 89.58 ± 0.36 | 88.68 ± 0.38 | 87.86 ± 0.29 2 | 86.73 ± 0.57 | 84.33 ± 0.31 |
0.1 NCE + 100 RCE | 88.92 ± 0.61 | 88.67 ± 0.73 | 88.34 ± 0.28 | 85.80 ± 0.16 2 | 84.90 ± 0.62 |
1 NCE + 0.1 RCE | 90.13 ± 0.27 | 88.96 ± 0.75 | 88.91 ± 0.41 | 87.32 ± 0.42 | 85.27 ± 0.36 |
1 NCE + 1 RCE | 89.89 ± 0.14 | 89.04 ± 0.47 | 88.32 ± 0.26 | 87.11 ± 0.11 | 83.82 ± 0.45 2 |
1 NCE + 10 RCE | 89.46 ± 0.61 | 88.84 ± 0.40 | 88.53 ± 0.15 | 85.93 ± 0.09 | 84.18 ± 0.52 |
1 NCE + 100 RCE | 88.89 ± 0.12 2 | 88.74 ± 0.39 | 88.02 ± 0.55 | 86.38 ± 0.33 | 85.02 ± 0.40 |
10 NCE + 0.1 RCE | 90.17 ± 0.83 1 | 89.68 ± 0.19 1 | 89.62 ± 0.35 1 | 87.70 ± 0.11 1 | 85.51 ± 0.78 1 |
10 NCE + 1 RCE | 89.62 ± 0.31 | 88.69 ± 0.19 | 88.60 ± 0.64 | 87.01 ± 0.50 | 84.69 ± 0.58 |
10 NCE + 10 RCE | 89.17 ± 0.25 | 88.60 ± 0.44 2 | 88.11 ± 0.10 | 86.38 ± 0.35 | 84.74 ± 0.36 |
10 NCE + 100 RCE | 89.76 ± 0.10 | 88.96 ± 0.24 | 88.19 ± 0.87 | 86.79 ± 0.34 | 84.26 ± 0.40 |
A-NCE + RCE (ours) | 90.60 ± 0.52 * | 90.12 ± 0.53 * | 90.04 ± 0.55 * | 88.37 ± 0.29 * | 86.88 ± 0.45 * |
Loss | Noise Rates | ||||
---|---|---|---|---|---|
Clean (0%) | 5.0% | 10.0% | 20.0% | 30% | |
NCE + RCE | 98.05 ± 0.16 | 97.58 ± 0.44 | 96.58 ± 0.24 | 95.92 ± 0.52 | 95.27 ± 0.31 |
A-NCE + RCE (ours) | 98.57 ± 0.40 | 98.70 ± 0.49 * | 97.72 ± 0.09 * | 96.97 ± 0.68 * | 96.68 ± 0.42 * |
The Weight of APL | The Type of APL | |||
---|---|---|---|---|
NCE + RCE | NCE + MAE | NFL + RCE | NFL + MAE | |
= 0.1 | 90.81 ± 0.66 | 90.75 ± 1.36 | 91.10 ± 0.62 | 90.05 ± 0.18 2 |
= 1 | 91.01 ± 0.66 | 91.67 ± 0.79 | 91.86 ± 1.07 | 92.07 ± 0.54 1 |
= 10 | 92.33 ± 0.67 | 90.70 ± 0.42 | 91.52 ± 1.10 | 90.87 ± 0.59 |
= 100 | 89.91 ± 0.67 2 | 91.59 ± 1.48 | 91.24 ± 0.13 | 90.77 ± 0.39 |
= 0.1 | 92.56 ± 0.31 1 | 91.34 ± 2.49 | 91.39 ± 0.13 | 91.15 ± 1.94 |
= 1 | 90.12 ± 0.50 | 91.29 ± 0.23 | 90.57 ± 0.51 | 91.61 ± 0.68 |
= 10 | 91.67 ± 0.93 | 91.76 ± 1.31 1 | 90.00 ± 0.71 2 | 91.33 ± 1.08 |
= 100 | 91.74 ± 0.29 | 90.48 ± 0.95 | 91.43 ± 0.98 | 90.76 ± 0.64 |
= 0.1 | 91.10 ± 1.67 | 88.73 ± 1.84 2 | 90.88 ± 2.83 | 90.23 ± 0.26 |
= 1 | 92.43 ± 1.26 | 91.68 ± 0.51 | 91.88 ± 0.41 1 | 91.95 ± 0.62 |
= 10 | 91.23 ± 1.19 | 91.64 ± 1.04 | 91.82 ± 0.74 | 90.42 ± 1.41 |
= 100 | 91.28 ± 1.45 | 90.51 ± 1.54 | 90.94 ± 1.37 | 91.12 ± 0.85 |
A-APL (ours) | 93.00 ± 0.32 * | 92.28 ± 0.88 * | 92.03 ± 0.79 * | 92.54 ± 0.29 * |
Methods | mAP | Methods | mAP |
---|---|---|---|
CE | 80.41 ± 0.52 | NCE + RCE [17] | 87.70 ± 0.11 |
MAE [13] | 86.53 ± 0.13 | NCE + MAE [17] | 85.82 ± 0.53 |
GCE [14] | 87.97 ± 0.48 | NFL + RCE [17] | 88.04 ± 0.32 |
RCE [15] | 86.39 ± 0.37 | NFL + MAE [17] | 84.07 ± 1.20 |
SCE [15] | 86.51 ± 0.19 | A-NCE + RCE (ours) | 88.37 ± 0.29 * |
ACE [16] | 86.52 ± 0.33 | A-NCE + MAE (ours) | 88.95 ± 0.45 * |
AUL [31] | 86.74 ± 0.82 | A-NFL + RCE (ours) | 88.76 ± 0.25 * |
AEL [31] | 85.79 ± 0.08 | A-NFL + MAE (ours) | 88.49 ± 0.26 * |
Backbone | Loss | mAP | Training Time (min) | FLOPs (G) |
---|---|---|---|---|
ResNet18 | CE | 69.48 ± 1.54 | 1.05 | 2.375 |
NCE + RCE | 87.21 ± 1.15 | 1.07 × 12 | 2.375 | |
A-NCE + RCE | 87.78 ± 0.78 * | 1.40 | 2.375 | |
ResNet50 | CE | 78.89 ± 0.87 | 1.83 | 5.368 |
NCE + RCE | 92.56 ± 0.31 | 1.88 × 12 | 5.368 | |
A-NCE + RCE | 93.00 ± 0.32 * | 2.98 | 5.368 | |
ResNet101 | CE | 79.55 ± 0.79 | 2.73 | 10.230 |
NCE + RCE | 89.72 ± 1.34 | 3.33 × 12 | 10.230 | |
A-NCE + RCE | 91.94 ± 0.67 * | 4.75 | 10.230 | |
DenseNet169 | CE | 87.46 ± 0.17 | 2.40 | 4.436 |
NCE + RCE | 94.93 ± 0.55 | 2.40 × 12 | 4.436 | |
A-NCE + RCE | 95.45 ± 0.50 * | 4.1 | 4.436 | |
MobileNetV3_large | CE | 81.18 ± 0.86 | 0.95 | 0.292 |
NCE + RCE | 89.63 ± 0.86 | 0.97 × 12 | 0.292 | |
A-NCE + RCE | 90.99 ± 0.33 * | 1.37 | 0.292 | |
MobileNetV3_small | CE | 75.43 ± 1.13 | 0.68 | 0.076 |
NCE + RCE | 84.59 ± 0.27 | 0.68 × 12 | 0.076 | |
A-NCE + RCE | 87.80 ± 0.15 * | 0.88 | 0.076 |
Metrics | 20% | 30% |
---|---|---|
Prediction probability | 91.17 ± 0.58 | 88.52 ± 0.05 |
Entropy | 91.83 ± 0.37 | 86.82 ± 1.95 |
S | 91.39 ± 0.74 | 87.38 ± 1.50 |
91.14 ± 0.59 | 87.94 ± 0.78 | |
RES | 92.44 ± 0.36 | 86.47 ± 1.42 |
93.00 ± 0.32 * | 90.01 ± 0.88 * |
tp = 1 | tp = 2 | tp = 3 | tp = 4 | tp = 5 |
---|---|---|---|---|
91.48 ± 1.05 | 92.25 ± 0.77 | 93.00 ± 0.32 * | 92.88 ± 0.47 | 92.55 ± 0.68 |
The Number of Hidden Layers | |||
---|---|---|---|
The Number of Neurons in Each Hidden Layer | 1 | 2 | 3 |
50 | 90.65 ± 0.72 | 91.99 ± 0.75 | 91.37 ± 0.97 |
100 | 93.00 ± 0.32 * | 91.41 ± 0.27 | 92.06 ± 0.18 |
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Tian, X.; Hou, D.; Wang, S.; Liu, X.; Xing, H. An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels. Appl. Sci. 2024, 14, 1756. https://doi.org/10.3390/app14051756
Tian X, Hou D, Wang S, Liu X, Xing H. An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels. Applied Sciences. 2024; 14(5):1756. https://doi.org/10.3390/app14051756
Chicago/Turabian StyleTian, Xueqing, Dongyang Hou, Siyuan Wang, Xuanyou Liu, and Huaqiao Xing. 2024. "An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels" Applied Sciences 14, no. 5: 1756. https://doi.org/10.3390/app14051756
APA StyleTian, X., Hou, D., Wang, S., Liu, X., & Xing, H. (2024). An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels. Applied Sciences, 14(5), 1756. https://doi.org/10.3390/app14051756