Severity: Warning
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Filename: drivers/Session_files_driver.php
Line Number: 177
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File: /var/www/html/index.php
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Function: require_once
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "choices"
Filename: controllers/Detail.php
Line Number: 249
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
Severity: Warning
Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
Severity: Warning
Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
Severity: 8192
Message: strpos(): Passing null to parameter #1 ($haystack) of type string is deprecated
Filename: models/Detail_model.php
Line Number: 71
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File: /var/www/html/application/models/Detail_model.php
Line: 71
Function: strpos
File: /var/www/html/application/controllers/Detail.php
Line: 252
Function: insertAPISummary
File: /var/www/html/index.php
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Function: require_once
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Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: str_replace
File: /var/www/html/application/controllers/Detail.php
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Function: formatAIDetailSummary
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "choices"
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
Severity: Warning
Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
Backtrace:
File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
Line: 316
Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 256
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Message: Undefined array key "usage"
Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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Many large and complex deep neural networks have been shown to provide higher performance on various computer vision tasks. However, very little is known about the relationship between the complexity of the input data along with the type of noise and the depth needed for correct classification. Existing studies do not address the issue of common corruptions adequately, especially in understanding what impact these corruptions leave on the individual part of a deep neural network. Therefore, we can safely assume that the classification (or misclassification) might be happening at a particular layer(s) of a network that accumulates to draw a final correct or incorrect prediction. In this paper, we introduce a novel concept of corruption depth, which identifies the location of the network layer/depth until the misclassification persists. We assert that the identification of such layers will help in better designing the network by pruning certain layers in comparison to the purification of the entire network which is computationally heavy. Through our extensive experiments, we present a coherent study to understand the processing of examples through the network. Our approach also illustrates different philosophies of example memorization and a one-dimensional view of sample or query difficulty. We believe that the understanding of the corruption depth can open a new dimension of model explainability and model compression, where in place of just visualizing the attention map, the classification progress can be seen throughout the network.
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http://dx.doi.org/10.1016/j.neunet.2023.11.035 | DOI Listing |
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