Modularized Morphing of Neural Networks
Authors: Tao Wei, Changhu Wang, Chang Wen Chen
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted based on the state-of-the-art Res Net on benchmark datasets, and the effectiveness of the proposed solution has been verified. |
| Researcher Affiliation | Collaboration | Tao Wei University at Buffalo Buffalo, NY 14260 taowei@buffalo.edu Changhu Wang Microsoft Research Beijing, China, 100080 chw@microsoft.com Chang Wen Chen University at Buffalo Buffalo, NY 14260 chencw@buffalo.edu |
| Pseudocode | Yes | Algorithm 1 Algorithm for Simple Morphable Modules |
| Open Source Code | No | The paper does not contain any statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | CIFAR10 (Krizhevsky & Hinton, 2009) is a benchmark dataset on image classification and neural network investigation. It consists of 32 32 color images in 10 categories, with 50,000 training images and 10,000 testing images. |
| Dataset Splits | Yes | CIFAR10 (Krizhevsky & Hinton, 2009) ... It consists of 32 32 color images in 10 categories, with 50,000 training images and 10,000 testing images." and "This dataset consists of 1,000 object categories, with 1.28 million training images and 50K validation images. |
| Hardware Specification | No | The paper does not mention any specific hardware (GPU, CPU model, memory, etc.) used for running the experiments. |
| Software Dependencies | No | The paper mentions training parameters like decay, momentum, optimizer (SGD), batch size, and learning rate schedule, but does not provide specific software dependencies with version numbers (e.g., libraries, frameworks). |
| Experiment Setup | Yes | In the training process, we follow the same setup as in (He et al., 2015). We use a decay of 0.0001 and a momentum of 0.9. We adopt the simple data augmentation with a pad of 4 pixels on each side of the original image. A 32 32 view is randomly cropped from the padded image and a random horizontal flip is optionally applied. |