Moreover,10 we will add one more predictiveperformance measure: F1 score. If the attribution map accurately represents the importance of the pixels, the20 classifier must havelower predictiveperformance. In [4], the authors introduce attention map transfer and gradient transfer. The function of the loss functions34 are different. The intent behind our stochastic matching regularizer is to facilitate42 the transfer of relevant information and prevent overfitting.

Our research expands on this area by examining non-smooth weakly-convex FCCO, where the outer function is weakly convex and non-decreasing, and the inner function is weakly-convex.

Thus, we did not create new threats to society. Moreover, our work provides a new perspective on backdoor defense, as it is the first to address the certification of backdoor detection. This assumption holds in general in practice. In our setting, this is reflected by a small samplewise local probability for the labeled class for most samples used for computing LDP, which may easily lead to a large LDP . In the following, we show that a larger deviation of the learned decision boundary of a binary Bayesian classifier will affect its LDP .

Backdoor attack is a common threat to deep neural networks.

The key idea of the proof is to exploit the problem representation in terms of confusion matrices. Here we set up and discuss the example in 3.2 in more detail. In the following, whenAj is used to denote an event inside a probability, it refers to the event {Aj =1}. First step is to extract the error incurred by plugging inˆη rather than η. C.2 WeightedERM In the weighed ERM approach (referred to as cost-sensitive classification for the binary case [1]) we parametrizeh: X [K]by a function classF of functions: X RK.