Conditional Image Generation with PixelCNN Decoders
Authors: Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, Alex Graves
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 compares Gated Pixel CNN with published results on the CIFAR-10 dataset. These architectures were all optimized for the best possible validation score, meaning that models that get a lower score actually generalize better. Gated Pixel CNN outperforms the Pixel CNN by 0.11 bits/dim, which has a very significant effect on the visual quality of the samples produced, and which is close to the performance of Pixel RNN. |
| Researcher Affiliation | Industry | Aäron van den Oord Google Deep Mind avdnoord@google.com Nal Kalchbrenner Google Deep Mind nalk@google.com Oriol Vinyals Google Deep Mind vinyals@google.com Lasse Espeholt Google Deep Mind espeholt@google.com Alex Graves Google Deep Mind gravesa@google.com Koray Kavukcuoglu Google Deep Mind korayk@google.com |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Table 1 compares Gated Pixel CNN with published results on the CIFAR-10 dataset. |
| Dataset Splits | No | The paper reports 'Test' and '(Train)' performance in tables, but does not explicitly provide specific percentages, sample counts, or detailed methodology for training/validation/test splits, beyond implying the existence of these sets. |
| Hardware Specification | No | The paper mentions '60 hours using 32 GPUs' for training, but does not specify the exact model or type of GPUs, CPU, memory, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions 'Tensor Flow [1]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | For the results in Table 2 we trained a larger model with 20 layers (Figure 2), each having 384 hidden units and filter size of 5 5. We used 200K synchronous updates over 32 GPUs in Tensor Flow [1] using a total batch size of 128. |