Collaborating Authors

Does Adversarial Transferability Indicate Knowledge Transferability? Artificial Intelligence

Despite the immense success that deep neural networks (DNNs) have achieved, adversarial examples, which are perturbed inputs that aim to mislead DNNs to make mistakes have recently led to great concern. On the other hand, adversarial examples exhibit interesting phenomena, such as adversarial transferability. DNNs also exhibit knowledge transfer, which is critical to improving learning efficiency and learning in domains that lack high-quality training data. In this paper, we aim to turn the existence and pervasiveness of adversarial examples into an advantage. Given that adversarial transferability is easy to measure while it can be challenging to estimate the effectiveness of knowledge transfer, does adversarial transferability indicate knowledge transferability? We first theoretically analyze the relationship between adversarial transferability and knowledge transferability and outline easily checkable sufficient conditions that identify when adversarial transferability indicates knowledge transferability. In particular, we show that composition with an affine function is sufficient to reduce the difference between two models when adversarial transferability between them is high. We provide empirical evaluation for different transfer learning scenarios on diverse datasets, including CIFAR-10, STL-10, CelebA, and Taskonomy-data - showing a strong positive correlation between the adversarial transferability and knowledge transferability, thus illustrating that our theoretical insights are predictive of practice.

LEEP: A New Measure to Evaluate Transferability of Learned Representations Machine Learning

We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.

Transferability and Hardness of Supervised Classification Tasks Machine Learning

We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables and exploring their statistics. When transferring from a source to a target task, we consider the conditional entropy between two such variables (i.e., label assignments of the two tasks). We show analytically and empirically that this value is related to the loss of the transferred model. We further show how to use this value to estimate task hardness. We test our claims extensively on three large scale data sets -- CelebA (40 tasks), Animals with Attributes 2 (85 tasks), and Caltech-UCSD Birds 200 (312 tasks) -- together representing 437 classification tasks. We provide results showing that our hardness and transferability estimates are strongly correlated with empirical hardness and transferability. As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.

Reducing Adversarial Example Transferability Using Gradient Regularization Machine Learning

Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples transfer between different architectures trained on different datasets. We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models. To demonstrate the relevance of this approach, we perform case studies that involve jointly training pairs of models. These case studies empirically justify the theoretical intuitions for why the angle between gradients is a fundamental quantity in AdvX transferability. Furthermore, we consider the asymmetry of AdvX transferability between two models of the same architecture and explain it in terms of differences in gradient norms between the models. Lastly, we provide a simple modification to existing training setups that reduces transferability of adversarial examples between pairs of models.

Deep Model Transferability from Attribution Maps

Neural Information Processing Systems

Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter. In this paper, we propose an embarrassingly simple yet very efficacious approach to estimating the transferability of deep networks, especially those handling vision tasks. Unlike the seminal work of \emph{taskonomy} that relies on a large number of annotations as supervision and is thus computationally cumbersome, the proposed approach requires no human annotations and imposes no constraints on the architectures of the networks. This is achieved, specifically, via projecting deep networks into a \emph{model space}, wherein each network is treated as a point and the distances between two points are measured by deviations of their produced attribution maps. The proposed approach is several-magnitude times faster than taskonomy, and meanwhile preserves a task-wise topological structure highly similar to the one obtained by taskonomy.