Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Deep networks were recently suggested to face the odds between accuracy and robustness.Such a dilemma is shown to be rooted in the inherently higher sample complexity and/or model capacity, for learning a high-accuracy and robust classifier.In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a “sweet point" in co-optimizing model accuracy, robustness, and efficiency.Our proposed solution, dubbed Robust Dynamic Inference Networks, allows for each input to adaptively choose one of the multiple output layers to output its prediction.That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.
Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins? Yes!Exploits input-adaptive multiple early-exits for the field of adversarial attack and defense, reducing the average inference complexity without conflicting the larger capacity assumption.