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Wekeep theteacher model fixed, while re-initializing thestudent model from9 the latest quantized version at each step. Here the P iWBi is summing up the values inWB, which can be pre-computed and stored as37 bias. QNLI Question Natural Language Inference (Wang et al., 2019) is a binary classification task50 whichisderivedfromtheStanfordQuestionAnsweringDataset(Rajpurkaretal.,2016). Semeval-2017task81 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation.arXiv

Figure 1: The example of samples involved in different backdoor watermarks. In the BadNets, blended attack, WaNet, and UBW-P, the labels of poisoned samples are inconsistent with their ground-truthones. In particular, since the label-consistent attack can only modify samples from the target73 class, itspoisoning rateissettoitsmaximum (i.e.,0.02)ontheImageNet dataset. Besides, following the classical settings in existing papers,75 we adopt awhite-black square as the trigger pattern for BadNets, blended attack, label-consistent76 attack, and UBW-P on both datasets. As shown in Table 2, the attack success rate increases with the increase of trigger size.128

First we remove the dependency on the integral by taking its lower33 boundandupperbound. This is the case whene KLα is large (see Figure 1a). This assumption holds as long as there is at least one task that is related to the main task (having59 a smallKLα), which is reasonable because if all the tasks are unrelated, then reweighing is also60 meaningless. Specifically, we find the results insensitive to the choice ofβ. Only 1000 out of 65392 images are147 labeled.

The generated individual image features are sequentially concatenated and convolved into group semantic representations, which are further equally concatenated backwithdifferent individuals toobtain co-saliencyfeatures.

Inspecific,wepropose5 a novel method, namely, SVD-Dictionary enhancement, to build two separated6 spaces based on the sorted singular values. Concretely, the eigenvectors corre-7 sponding to larger singular values are used to build the generalization space in8 which localization isperformed, asthese eigenvectors generally suppress certain9 variations (e.g., the variation of styles) and contain intrinsical characteristics of10 objects.

Weuse the twohighest frequencyones which result in 776 label categories. Thelearningrateis12 decreased by afactor of 10 atthe end of 10th and 20th epochs. The networks are trained for 36epochs. Since the all the labels for the test images are not annotated, we only evaluate the performance of17 our model on the set of annotated labels. Hence false positive can happen only if apositively18 annotated label is predicted as a negative class.