The first termkxt x αk2 can be simply merged in eq. The first term is consensus error and will be merged inT1 in eq. Response: We have conducted more experiments over the ImageNet37 dataset which is known as a complicated dataset. As the middle figure38 demonstrates, for a binary classification, our method significantly im-39 proves upon the benchmarks over this dataset as well. We also carried40 out experiments on a deeper neural network with 4 hidden layers and41 ourmethod providessignificant speedups overthebenchmarks (bottom42 figure).

For the generator and the three discriminators, we use the FFHQ [2] and AAHQ datasets with 1024 1024 resolution. Hence, cooperating withGAN inversion methods, our framework is able to achieve arbitrary style transfer of a given face image. Wheni=0,allthelayersofthegenerator areinfluenced bythestylelatentcode. Result images of the directly concatenating method have similar face identities and head poses to their reference images, which means that this method leaks content information ofreference images to stylelatentcodes. However, for a reference image whose style is significantly different from that inAAHQ, ifdirectly feeding itinto BlendGAN, the style ofgenerated images maynotbesimilartothereference.

Hence, the above formulation of set function is submodular and is an instance of concave over modular function. Inour setting, we use labeled target dataDt asthe validation set. The completed-SNE loss is defined as a combination of Land cross-entropy loss on source and targetdomain. Table 8 shows the training times for this setting. Again, we see that all instantiationsofORIENTachieve 2.5 speed-upcomparedtoFull.

The recent success of deep learning frameworks in applications such as image classification [9], speech recognition [20], and object detection [13] stems primarily from the availability of large amounts of labeled data.

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Weappreciate this point and will revisit the word choice. What is given to the turkers? We will provide the full prompt in revision along with other details (we used 327 annotators) and discussion. For overall trustworthiness for instance, we asked "Does the article read like it comes28 from a trustworthy source?" Nevertheless, BERT is worse at neural fake news discrimination compared with Grover.

Bottleneck Models (CBMs) have gained popularity since their introduction.