Training deep neural-networks using a noise adaptation layer
Authors: Jacob Goldberger, Ehud Ben-Reuven
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that this approach outperforms previous methods. |
| Researcher Affiliation | Academia | Engineering Faculty, Bar-Ilan University, Ramat-Gan 52900, Israel |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The network we implemented is publicly available 1. 1code available at https://github.com/udibr/noisy_labels |
| Open Datasets | Yes | The MNIST is a database of handwritten digits, which consists of 28 28 images. The dataset has 60k images for training and 10k images for testing. |
| Dataset Splits | Yes | The dataset has 60k images for training and 10k images for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and 'Re LU' activation but does not specify versions for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We used a two hidden layer NN comprised of 500 and 300 neurons. The non-linear activation we used was Re LU and we used dropout with parameter 0.5. We trained the network using the Adam optimizer (Kingma & Ba (2014)) with default parameters, which we found to converge more quickly and effectively than SGD. We used a mini-batch size of 256. These settings were kept fixed for all the experiments described below. |