Note that for error injection experiments, we perform single-bit flips only in the convolutional and linear layers of the neural network, in line with other work in this field. In this section, we provide visualizations of additional backbones. Figure 9 and Figure 10 extend from Figure 3 for more networks. The Y -axis shows the absolute value of the max neuron value observed per layer on the X-axis. Next, Figure 11 and Figure 12 are extensions for Figure 4, showcasing the impact of our proposed technique on the end-to-end network accuracy.

Seq2Seq AP - 44.8 (e) Large vocabulary object detection.

They map random samples (seeds) from a high-dimensional space, conditioned on a user-provided text prompt, to a corresponding image.

Most applications of machine learning to classification assume a closed set of balanced classes.

Key attributes (cf. Figure 1) include the challenging task of classifying doppelganger animals,

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