The State of Sparsity in Deep Neural Networks
We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex bayesian techniques shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Based on insights from our experiments, we achieve a new state-of-the-art sparsity-accuracy trade-off for ResNet-50 using only magnitude pruning. Additionally, we consider the contributions of research work that suggests pruning is a type of architecture search. We find that on our large scale setup, that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization.
paper, code
We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex bayesian techniques shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Based on insights from our experiments, we achieve a new state-of-the-art sparsity-accuracy trade-off for ResNet-50 using only magnitude pruning. Additionally, we consider the contributions of research work that suggests pruning is a type of architecture search. We find that on our large scale setup, that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization.
paper, code
Evaluating Feature Importance Estimates
Interpretability methods should be both meaningful to a human and correctly explain model behavior. In this work, we propose ROAR, RemOve And Retrain, a formal measure to evaluate the latter. ROAR measures the relative accuracy of commonly used interpretability methods and a set of recently proposed ensemble-based derivative approaches. Our results across several large-scale image classification datasets are consistent and thought-provoking -- we find that the interpretability methods we consider produce estimates that are less accurate or on par with a random designation of feature importance. However, certain derivative approaches that ensemble these estimates far outperform such a random guess. The manner of ensembling remains critical, we show that some approaches do no better than the underlying method but carry a far higher computational burden.
Slides, code, paper.
Interpretability methods should be both meaningful to a human and correctly explain model behavior. In this work, we propose ROAR, RemOve And Retrain, a formal measure to evaluate the latter. ROAR measures the relative accuracy of commonly used interpretability methods and a set of recently proposed ensemble-based derivative approaches. Our results across several large-scale image classification datasets are consistent and thought-provoking -- we find that the interpretability methods we consider produce estimates that are less accurate or on par with a random designation of feature importance. However, certain derivative approaches that ensemble these estimates far outperform such a random guess. The manner of ensembling remains critical, we show that some approaches do no better than the underlying method but carry a far higher computational burden.
Slides, code, paper.
The (Un)reliability of Saliency Methods
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step —adding a constant shift to the input data— to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute.
In order to guarantee reliability, we posit that methods should fulfill input invariance, the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input. We show, through several examples, that saliency methods that do not satisfy input invariance result in misleading attribution.
Paper, Slides.
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step —adding a constant shift to the input data— to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute.
In order to guarantee reliability, we posit that methods should fulfill input invariance, the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input. We show, through several examples, that saliency methods that do not satisfy input invariance result in misleading attribution.
Paper, Slides.