VR
V.C.J.R. Rullens
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In recent years, strong progress has been made in creating learnable affine-equivariant models for downstream tasks such as classification. However, these models encounter increased data requirements to represent all possible transformations due to greater task complexity, while having been shown to generalize poorly to out-of-distribution data. In this work, we introduce a test-time approach for generalizing to out-of-distribution data. Namely, by utilizing a network trained to reconstruct any image that is part of a standardized training distribution, our model can infer an affine transform that moves new samples in-distribution by minimizing their reconstruction loss. With this, this approach closely matches the work of Spatial Transformer Networks, which instead learn to transform data, and inverted neural renderers for pose estimation. Through experiments, we show that this method contains a strong level of out-of-distribution translation and scale invariance, as well as a small level of rotation invariance. Namely, we show that it can handle significant transformations beyond those produced by commonly used benchmarks such as AffNIST. Using this strength we show that this method excels especially in low data regimes, outperforming existing competitors.
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In recent years, strong progress has been made in creating learnable affine-equivariant models for downstream tasks such as classification. However, these models encounter increased data requirements to represent all possible transformations due to greater task complexity, while having been shown to generalize poorly to out-of-distribution data. In this work, we introduce a test-time approach for generalizing to out-of-distribution data. Namely, by utilizing a network trained to reconstruct any image that is part of a standardized training distribution, our model can infer an affine transform that moves new samples in-distribution by minimizing their reconstruction loss. With this, this approach closely matches the work of Spatial Transformer Networks, which instead learn to transform data, and inverted neural renderers for pose estimation. Through experiments, we show that this method contains a strong level of out-of-distribution translation and scale invariance, as well as a small level of rotation invariance. Namely, we show that it can handle significant transformations beyond those produced by commonly used benchmarks such as AffNIST. Using this strength we show that this method excels especially in low data regimes, outperforming existing competitors.
The impact of Graph Neural Network task types on the stability of Graph Neural Networks in face of perturbations
A coded experiment on GNN stability
Graph Neural Networks (GNN) are Machine Learning models which are trained on graph data in order to handle complex state-of-the-art tasks such as recommender systems and molecular property prediction. However, the graphs that these models are trained on can be perturbed in various ways post training resulting in reductions in performance. This study compares the stability of various Graph Neural Network task types (Node Classification, Link Prediction, and Graph Classification) by investigating each of their performances across a range of perturbation severities performed on graphs. Further experiments explore whether this performance ranking changes for different types of perturbations and GNN architectures. Through results, it is shown that there is a noticeable difference in stability between the investigated tasks. However, it is also shown that under certain conditions, such as different types of perturbations or architectures, the performance ranking may shift. This paper highlights weak points in GNNs that should be explored for stronger defenses against potential attacks.
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Graph Neural Networks (GNN) are Machine Learning models which are trained on graph data in order to handle complex state-of-the-art tasks such as recommender systems and molecular property prediction. However, the graphs that these models are trained on can be perturbed in various ways post training resulting in reductions in performance. This study compares the stability of various Graph Neural Network task types (Node Classification, Link Prediction, and Graph Classification) by investigating each of their performances across a range of perturbation severities performed on graphs. Further experiments explore whether this performance ranking changes for different types of perturbations and GNN architectures. Through results, it is shown that there is a noticeable difference in stability between the investigated tasks. However, it is also shown that under certain conditions, such as different types of perturbations or architectures, the performance ranking may shift. This paper highlights weak points in GNNs that should be explored for stronger defenses against potential attacks.