SlowGuess/ACL_2020_OCR huggingface dataset:
classes are randomly split into 160 "seen" classes for training and validation (within which 120 classes are randomly selected for training, and 40 for validation) and 40 "unseen" classes for testing, and a 40-class classification model is generated by  $\mathbf{N}^3$ . Within the seen classes,  $20\%$  of the dataset is are excluded since (Lei Ba et al., 2015) used them for testing to report classification results on seen classes, which is irrelevant to our comparisons. Meta models are trained using the 160 seen classes only. Similarly, in Oxford Flowers, the 102 classes of flowers are split into 82 "seen" classes and 20 "unseen" classes randomly. We used a ResNet-18 pre-trained on ImageNet as our base model. Note that while we didn't adopt the larger VGG-19 base model as previous work, later we will show that we are still able to outperform them significantly with the lesser ResNet-18 architecture. During test time,  $\mathbf{N}^3$  generates neural networks based on test set class descriptions and these generated networks are used for zero-shot evaluation.
As shown in Table 6,  $\mathbf{N}^3$  generated ResNet18 out-performs prior arts by a significant margin both in terms of average precision and classification accuracy on both CUB and Oxford Flower dataset.
We then move on to evaluate the performance of  $\mathbf{N}^3$  on more rigorous and difficult zero-shot metasplits on the CUB-2011 and NAB dataset. This split is called SCE (super category exclusive) split, which ensures that the parent categories of unseen classes are exclusive to those of the seen classes. Such split is used in many prior works (Zhu et al., 2017; Elhoseiny et al., 2017; Changpinyo et al., 2016) to evaluate their proposed method. We evaluated  $\mathbf{N}^3$  using such meta-split and results are shown in Table 5. As explained in the experimental evaluation section of our paper, later prior works (Zhu et al., 2017; Elhoseiny et al., 2017) uses significantly more metadata (e.g., per sample parts annotation) than  $\mathbf{N}^3$  and yet  $\mathbf{N}^3$  is able to exceed their classification accuracy by using a generic ResNet-18 base model.
C Magnitude of Parameter Adaptation
In this section, we visualize in Fig. 6, the magnitude of  $\mathrm{N}^3$  's parameter adaptation per layer (quantified by the L2 norm of each layer's parameter adaptation tensor generated by  $\mathrm{N}^3$ ) when adapting pre-trained ResNet-18.
D Hyper Parameters
We tabulated the set of hyper parameters used to produce our experimental results in Table. 7.
E Computational Resource Consumption
We used two types of machines - 4 x NVidia Pascal-100 and 4 x NVidia Volta-100 - depending on availability. It is worth noting that computation was not a bottleneck for our experiments; and a multi-GPU setup is primarily used for their larger total memory size available since transformers are known to consume a lot of memory when processing lengthy sequences. We conjecture that recent development of more efficient variants of transformer models would enable N3 to train with fewer GPUs. The training time ranged from a few hours to a single day depending on the size of the dataset and the computing power of the machine.
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Dataset	Method	A.P.	Acc.@1	Acc.@5	Base Model
CUB-2010	DA (VGG) (Kulis et al., 2011)	0.037	-	-	VGG-19
PDCNN(fc) (Lei Ba et al., 2015)	0.1	12.0%	42.8%	VGG-19
PDCNN(conv) (Lei Ba et al., 2015)	0.043	-	-	VGG-19
PDCNN(fc+conv) (Lei Ba et al., 2015)	0.08	-	-	VGG-19
Ours(ResNet18)	0.31	33.9%	77.0%	ResNet-18
CUB-2011	PDCNN(fc) (Lei Ba et al., 2015)	0.11	-	-	VGG-19
PDCNN(conv) (Lei Ba et al., 2015)	0.085	-	-	VGG-19
PDCNN(fc+conv) (Lei Ba et al., 2015)	0.13	-	-	VGG-19
Ours(ResNet18)	0.27	32.6%	68.6%	ResNet-18
Flowers	PDCNN(fc) (Lei Ba et al., 2015)	0.07	-	-	VGG-19
PDCNN(conv) (Lei Ba et al., 2015)	0.054	-	-	VGG-19
PDCNN(fc+conv) (Lei Ba et al., 2015)	0.067	-	-	VGG-19
Ours(ResNet18)	0.16	10.4%	39.7%	ResNet-18
Table 6:  ${\mathrm{N}}^{3}$  Zero-Shot Classification Performance on CUB-2010, CUB-2011, Oxford Flower
Method	CUB Acc.@1	NAB Acc.@1
WAC-Linear (Elhoseiny et al., 2016)	5 %	-
WAC-Kernel (Elhoseiny et al., 2016)	7.7 %	6.0 %
ESZSL (Romera-Paredes and Torr, 2015)	7.4 %	6.3 %
ZSLSNS (Qiao et al., 2016)	7.3 %	6.8 %
SynCfast (Changpinyo et al., 2016)	8.6 %	3.8
SynCOVO (Changpinyo et al., 2016)	5.9 %	-
ZSLPP (Elhoseiny et al., 2017)	9.7 %	8.1 %
GAZSL (Zhu et al., 2017)	10.3 %	8.6 %
CorrectionNetwork (Hu et al., 2019)	10.0 %	9.5 %
Ours(ResNet18)	11.9 %	11.1 %
Figure 5: Zero-Shot Classification Performance on CUB-2011/NAB with SCE-split.
Figure 6: Magnitude of Parameter Adaptation
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(Max) Training Epoch	T2L Num Layer	L2A Num Layer	T2L LR	L2A LR
CUB-SS/PS	40	2/1	1/1	3e-5/1e-5	5e-5/1e-5
AWA-SS/PS	20	2/1	1/1	2e-6/9e-6	1e-5/2e-6
NAB-SS/PS	25	2/2	1/1	2e-5/1e-5	2e-5/2e-5
FS-SS/PS	40	2/2	1/1	2e-5/1e-6	1e-6/1e-5
Figure 7: Hyper-parameters
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