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Changed PyTorch estimators to use PyTorch datasets and dataloaders to optimize the fit and predict methods for PyTorchClassifier, PyTorchRegressor, PyTorchRandomizedSmoothing, PyTorchObjectDetector, and PyTorchYolo and optimized the predict method of TensorFlowV2Classifier by using a TensorFlow dataset and applying @tf.function decorator (Optimize PyTorch Classifiers and Object Detectors #2180)
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This release of ART 1.15.0 introduces
Added
TensorFlowV2Classifier
(Implement Default Training Loop forTensorFlowV2Classifier
#2124)BadDet
poisoning attacks (BadDet Poisoning Attacks Extension #2189)Changed
fit
andpredict
methods forPyTorchClassifier
,PyTorchRegressor
,PyTorchRandomizedSmoothing
,PyTorchObjectDetector
, andPyTorchYolo
and optimized thepredict
method ofTensorFlowV2Classifier
by using a TensorFlow dataset and applying @tf.function decorator (Optimize PyTorch Classifiers and Object Detectors #2180)PyTorchObjectDetector
to applychannels_first
argument and improved performance by applying batch processing provided by newer PyTorch versions. (Optimize PyTorch Classifiers and Object Detectors #2180)Removed
[None]
Fixed
SignOPTAttack
(Remove incorrectly duplicated queries in SignOPT attack #2129)ProjectedGradientDescentPyTorch
(Fix Tensor Device Inconsistencies in PGD #2135)PyTorchYolo
estimator and updated format of targets passed to the estimator inAdversarialPatchPyTorch
to reflect updates toPyTorchYolo
(Fix AdversarialPatchPyTorch compatibility with YOLO estimator #2169)analyze_by_distance
andanalyze_by_size
ofClusteringAnalyzer
(Fix ragged nested sequence warning #2195)This discussion was created from the release ART 1.15.0.
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