SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 6 demonstrates the effect of the number of levels  $L$  on image reconstruction benchmarks of FFHQ images with  $256 \times 256$ ,  $512 \times 512$ , and  $1024 \times 1024$  resolutions. Our INR decoder uses bandwidths  $\sigma_{\mathrm{q}} = 16$  and  $(\sigma_{\ell})_{\ell=1}^{L}$  equal to (128), (128,32), (128,64,32) and (128,90,64,32) for  $L = 1,2,3,4$  respectively in case of  $256 \times 256$  and  $512 \times 512$  resolution, and all bandwidths are doubled for  $1024 \times 1024$  to leverage high-frequency details.
Note that our framework outperforms previous studies [8, 19] even with  $L = 1$ . Moreover, the results demonstrate that increasing  $L$  improves
the performance, while the performance saturates beyond  $L \geq 3$ . We postulate that higher resolution requires a larger number of levels, as the performance gap between  $L = 3$  and  $L = 4$  decreases as the resolution increases.
Table 6: PSNRs on the reconstructed FFHQ with  $256\times 256$ ,  $512\times 512$ , and  $1024\times 1024$  resolutions for different number of levels.
256×256	512×512	1024×1024
TransINR	30.96	29.35	-
IPC [19]	34.68	31.58	28.68
Ours (L=1)	37.09	34.84	31.56
Ours (L=2)	39.88	35.43	31.94
Ours (L=3)	40.13	35.58	32.40
Ours (L=4)	39.79	35.40	32.32
B.2 Additional Examples of Novel View Synthesis
In Figure 8, we show additional examples of novel view synthesis of ShapeNet Chairs, Cars, and Lamps with one to five support views.
B.3 Additional Examples of High-resolution Image Reconstruction
Figure 9 and 10 shows image reconstruction examples of FFHQ with  $256 \times 256$ ,  $512 \times 512$ , and  $1024 \times 1024$  resolution by previous studies [8, 19] and our locality-aware generalizable INR. Unlike previous studies, our framework can successfully reconstruct fine-grained details in high resolutions.
Figure 8: Examples of novel view synthesis of ShapeNet Chairs, Cars and Lamps with one, two, three, and five support views.
Figure 9: Examples of reconstructed images of FFHQ with  $256 \times 256$  resolution (top row) and  $512 \times 512$  resolution (bottom row) by TransINR [8] (left), IPC [19] (middle), and our locality-aware generalizable INR (right).
Figure 10: Examples of reconstructed images of FFHQ with  $1024 \times 1024$  resolution by IPC (left) and our locality-aware generalizable INR (right).
Figure 11: Additional examples of class-conditional image synthesis by generating the locality-aware latents of our framework via a transformer-based diffusion model with 458M parameters. All images are generated with classifier-free guidance at scale 2.5.
Figure 12: Additional visualization of differences between model predictions after replacing a latent token with the zero vector for IPC [19] and our framework.
B.4 Additional Examples of Conditional Image Synthesis
Figure 11 shows additional examples of generated images with  $256 \times 256$  resolution by generating locality-aware latents of our framework.
B.5 Additional Visualization for Locality Analysis
Figure 12 visualizes which local information of data is encoded in each latent token of IPC [19] and our locality-aware generalizable INR in addition to Figure 5. We randomly select a latent token and replace it with the zero vector, then visualize the difference between the model predictions with or without the replacement as described in Section 4.3. The differences are rescaled to have the maximum value of 1 for clear visualization. Furthermore, we fix the set of replaced latent tokens for different samples in Figure 12 to emphasize the role of each latent token. Note that each latent token of our framework encodes the local information in a particular region of images or light fields, while latent tokens of IPC encode global information over whole coordinates.
B.6 Ablation Study on Linear Layers in Selective Token Aggregation
Our framework adds a linear layer in Eq (6) and Eq (7) to exploit complex frequency patterns, improving the performance. While the Fourier features consist of periodic patterns along an axis, the frequency patterns in Eq (6) can also include non-periodic patterns. Note that IPC [19] also uses a similar design, while modulating the second MLP layer to exploit complex frequency patterns. The linear layer in Eq (7) is used to process the modulation vector according to each frequency bandwidth, motivated by the design of separate projections for (query, key, value) in
self-attention. The results below also show that removing the linear layers in Eq (6) and Eq (7) significantly deteriorates the image reconstruction performance on ImageNette  $178 \times 178$ .
Table 7: PSNRs on the reconstructed ImageNette with  $178\times 178$  resolution.
PSNR
Ours	37.46
w/o Linear in Eq (6)	31.95
w/o Linear in Eq (7)	32.07
w/o Linear in Eq (6) and Eq (7)	31.57