On the Frequency-bias of Coordinate-MLPs
Authors: Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 8 Experiments In this section, we will show that the insights developed thus far extend well to deep networks in practice. 8.1 Encoding signals with uneven sampling ... Fig. 3 shows an example for encoding a 1D signal. Fig. 4 illustrates a qualitative example in encoding a 2D image. Table 1 depicts quantitative results on the natural dataset by Tancik ... Table 2: Encoding images with sparse sampling. ... Table 3: Quantitative comparison in novel view synthesis on the real synthetic dataset [Mildenhall et al., 2020]. |
| Researcher Affiliation | Academia | Sameera Ramasinghe Lachlan Macdonald {firstname.lastname}@adelaide.edu.au University of Adelaide Simon Lucey |
| Pseudocode | No | The paper does not contain any blocks explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | Table 1 depicts quantitative results on the natural dataset by Tancik et al. [2020]... Table 2: ... over the STL dataset Coates et al. [2011] and a sub-sampled version of Image Net with 10% sampling. |
| Dataset Splits | No | No explicit percentages or counts for train/validation/test splits are provided, nor is a specific validation set mentioned or described for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or types used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | This example uses a 4-layer sinusoid-MLP trained with 33% of the total samples. ... The total loss function for the coordinate-MLP then becomes Ltotal = LMSE + εLr where ε is a small scalar coefficient and LMSE is the ususal mean squared error loss. ... We use 4-layer networks for this experiment. |