SlowGuess/NeurIPS_2023_OCR huggingface dataset:
where  $\phi_{i}$  denotes context vector for  $i$ -th sample in training set,  $\mathcal{N}(i)$  is a set of indices of the neighborhood of sample and is sampled from the uniform distribution ranged between 0 to 1.
This sampling by interpolation scheme achieves 59.67 (FID) while sampling from diffusion model does 84.64 (FID). From the results, two major observations can be made: Sampling through interpolation yields a superior FID score than the latent diffusion model, suggesting issues with the latter's interaction with context vectors. The FID score for interpolation sampling, while better than diffusion, remains suboptimal. The observation that the context vector interpolation is not fully effective for the CIFAR10 model indicates a less smooth latent space in CIFAR10 compared to other datasets. This disparity could be impacting the generation quality.
We hypothesize that the limitations of the SIREN [33] architecture might be at the root of these issues. An INR work [39] categorizes SIREN as an INR with predominantly frequency encodings and a lack of space resolution, leading to compromised content quality. A separate study [5] underlines this, stating SIREN's unsatisfactory generation performance (FID 78.2) when applied to CIFAR10. This research introduces a new SIREN architecture where biases in hidden layers of INR are varying with respect to spatial resolution. We anticipate that adopting an INR with spatial information as an alternative to SIREN will better accommodate diverse datasets, given that our approach—a linear combination of INR weights—is broadly compatible with various INR methods.
The proposed image model is trained on the CelebA-HQ  $64^{2}$  dataset, which raises the possibility of unintended bias in the generated human face images due to the biases present in the dataset. Additionally, when extending the generative neural field model to large-scale datasets, there is a potential for generating unintended biased contents, although the current version of the work primarily focuses on small-scale datasets. We acknowledge that these concerns are important themes that should be thoroughly discussed in the future.
(a) CelebA-HQ  $64^{2}$
(b) ShapeNet  $64^{3}$
Figure 5: Generated samples from our models trained on CelebA-HQ  $64^{2}$  (5a) and ShapeNet  $64^{3}$  (5b).
Figure 6: Generated 25 samples with diverse 9 views from our model trained on SRN Cars.
Figure 7: Reconstructed samples from latent vectors for images (7a) and voxels (7b, 7c). Samples with green boxes are reconstructed instances from latent vectors estimated from train split for each dataset. The other samples are generated with bilinear interpolation of latent vectors from corner instances. In the third row 7c, we plot three interpolations with four different classes correspondingly. (left) chair (top left), cabinet (top right), display (bottom left), sofa (bottom right) (middle) loudspeaker, sofa, airplane, bench (right) airplane, bench, car, rifle. We use Mitsuba3 renderer for drawing voxel image.
(b) ShapeNet  $64^{3}$  (intra-class)
(c) ShapeNet  $64^{3}$  (inter-class)
(a) View 0
(b) View 32
(d) View 96
(e) View 128
(g) View 192
(h) View 224
(f) View 160
(i) View 250
Figure 8: Reconstructed view images from latent vector for NeRF scene. Image annotated by green boxes are synthesized instances from latent vectors estimated from SRN Cars train split. We select 9 views from the pre-defined 251 views in test split. The other samples are generated by bilinear interpolation of samples at the corners.