This release of ART 1.15.0 introduces a default training loop for TensorFlowV2Classifier, the TRADES adversarial training protocol, an estimator for DEtection TRansformer (DETR) object detection models, and more.

Added

• Added default training function to TensorFlowV2Classifier (#2124)
• Added TRADES adversarial training protocol in PyTorch (#2131)
• Added preprocessors for images supporting padding and resizing in PyTorch, TensorFlow and framework-independent (#2138)
• Added support for arbitrarily sized images in BadDet poisoning attacks (#2189)
• Added estimator for DEtection TRansformer (DETR) object detection models based on transformer architectures (#2192)

Changed

• 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 (#2180)
• Changed PyTorchObjectDetector to apply channels_first argument and improved performance by applying batch processing provided by newer PyTorch versions. (#2180)

Removed

[None]

Fixed

• Fixed unnecessary duplicate prediction calls to estimator in SignOPTAttack (#2129)
• Fixed missing transfer of tensor to device in ProjectedGradientDescentPyTorch (#2135)
• Fixed trigger placement for image poisoning perturbations by correctly accessing height and width of the trigger image instead of swapping both (#2143)
• Fixed key error in loss gradients of PyTorchYolo estimator and updated format of targets passed to the estimator in AdversarialPatchPyTorch to reflect updates to PyTorchYolo(#2169)
• Fixed Visible Deprecation Warning in analyze_by_distance and analyze_by_size of ClusteringAnalyzer (#2195)