Label Memorization of CIFAR-10N
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下载链接：
https://zenodo.org/record/14687825
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资源简介：
These files contain results from running heldout estimation on the CIFAR-10N dataset with the rand1 noise type, using 1500 ResNet models. The cifar-rand1-human-infl-mem.npz file contains results for human noisy labels, while cifar-rand1-syn-infl-mem.npz contains results for synthetic noisy labels generated using the same class transition matrix.
Dictionary Keys
1. total_runs
Type: Integer
Description: The total number of training runs included in this aggregated result.
2. trainset_mask
Type: array (Boolean, shape: [total_runs, train_size])
Description: A mask indicating which training examples were used (True) or held out (False) during each training run.
3. trainset_correctness
Key: trainset_correctness
Type: array (Boolean, shape: [total_runs, train_size])
Description: Whether the model correctly predicted the label for each training example during each run.
4. trainset_predictions
Type: array (Integer, shape: [total_runs, train_size])
Description: The predicted class labels for each training example during each run.
5. testset_correctness
Type: array (Boolean, shape: [total_runs, test_size])
Description: Whether the model correctly predicted the label for each test example during each run.
6. testset_predictions
Type: array (Integer, shape: [total_runs, test_size])
Description: The predicted class labels for each test example during each run.
7. memorization
Type: array (Float, shape: [train_size])
Description: Memorization score of each training example, computed across all runs.
8. influence
Type: array (Float, shape: [test_size, train_size])
Description: Influence scores of each training example on each test example.
9. memorization_inclusion_prob
Type: array (Float, shape: [train_size])
Description: Probability that the training example is predicted correctly when included.
10. memorization_exclusion_prob
Type: array (Float, shape: [train_size])
Description: Probability that the training example is predicted correctly when excluded.
Usage
To load the file and access its contents:
import numpy as np
# Load the file
data = np.load('cifar-rand1-human-agg-infl-mem.npz')
# Access individual components
total_runs = data['total_runs']
trainset_mask = data['trainset_mask']
memorization = data['memorization']
Notes
Our results were aggregated over 1500 training runs.
For more details or questions, feel free to reach out!
创建时间：
2025-03-18
