Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Authors: Hyeonseob Nam, Hyo-Eun Kim
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that the proposed approach surprisingly outperforms BN in general object classification and multi-domain problems, and it also successfully substitutes IN in style transfer networks. |
| Researcher Affiliation | Industry | Hyeonseob Nam Lunit Inc. Seoul, South Korea hsnam@lunit.ioHyo-Eun Kim Lunit Inc. Seoul, South Korea hekim@lunit.io |
| Pseudocode | No | The paper provides mathematical equations for Batch-Instance Normalization but does not include formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | BIN can be implemented with only a few lines of code using popular deep learning frameworks.1; 1https://github.com/hyeonseob-nam/Batch-Instance-Normalization |
| Open Datasets | Yes | We first evaluate BIN for general object classification using CIFAR-10/100 [15] and Image Net [18] datasets. ... We employ the Office-Home dataset [23] ... using content images from the MS-COCO dataset [17] |
| Dataset Splits | Yes | Table 1: Top-1 accuracy (%) on CIFAR-10/100 (test set) and Image Net (validation set) evaluated with Res Net-110 and Res Net-18, respectively. ... On CIFAR datasets, we train the networks for 64K iterations, where the learning rate is divided by 10 at 32K and 48K iterations; on Image Net, training runs for 90 epochs, where the learning rate is divided by 10 at 30 and 60 epochs. |
| Hardware Specification | No | The paper describes the network architectures and training procedures but does not specify the hardware (e.g., GPU models, CPU types) used for experiments. |
| Software Dependencies | No | The paper mentions 'popular deep learning frameworks' and 'publicly available implementation2 of classification networks' (footnote 2 links to 'pytorch-classification') but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | The networks are trained by SGD with a batch size of 128, an initial learning rate of 0.1, a momentum of 0.9, and a weight decay of 0.0001. On CIFAR datasets, we train the networks for 64K iterations, where the learning rate is divided by 10 at 32K and 48K iterations; on Image Net, training runs for 90 epochs, where the learning rate is divided by 10 at 30 and 60 epochs. |