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Found 9 papers, 2 papers with code
TestAug: A Framework for Augmenting Capability-based NLP Tests
Our experiments show that TestAug has three advantages over the existing work on behavioral testing: (1) TestAug can find more bugs than existing work; (2) The test cases in TestAug are more diverse; and (3) TestAug largely saves the manual efforts in creating the test suites.
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
To ensure an AdNN satisfies the performance requirements of resource-constrained applications, it is essential to conduct performance testing to detect IDPBs in the AdNN.
NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems
Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy.
CorrGAN: Input Transformation Technique Against Natural Corruptions
These DNNs are vulnerable to adversarial perturbations and corruptions.
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models.
EREBA: Black-box Energy Testing of Adaptive Neural Networks
The test inputs generated by EREBA can increase the energy consumption of AdNNs by 2, 000% compared to the original inputs.
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems
To further the understanding of such efficiency-oriented threats and raise the community’s concern on the efficiency robustness of NMT systems, we propose a new attack approach, TranSlowDown, to test the efficiency robustness of NMT systems.
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs
Unlike the memory cost, the energy consumption of the Neural ODEs during inference can be adaptive because of the adaptive nature of the ODE solvers.
ILFO: Adversarial Attack on Adaptive Neural Networks
With the increasing number of layers and parameters in neural networks, the energy consumption of neural networks has become a great concern to society, especially to users of handheld or embedded devices.
