Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Authors: Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101). |
| Researcher Affiliation | Academia | Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller and Thomas Brox Department of Computer Science University of Freiburg 79110, Freiburg im Breisgau, Germany {dosovits,springj,riedmiller,brox}@cs.uni-freiburg.de |
| Pseudocode | No | The paper describes the learning algorithm and formulations in text and equations, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and training data are available at http://lmb.informatik.uni-freiburg.de/resources . |
| Open Datasets | Yes | To compare our discriminative approach to previous unsupervised feature learning methods, we report classification results on the STL-10 [21], CIFAR-10 [22] and Caltech-101 [23] datasets. |
| Dataset Splits | Yes | On STL-10 the SVM was trained on 10 pre-defined folds of the training data. ... For Caltech-101 we followed the usual protocol of selecting 30 random samples per class for training and not more than 50 samples per class for testing. This was repeated 10 times. |
| Hardware Specification | No | The paper mentions training networks using Caffe but does not provide any specific details about the hardware used (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions using 'Caffe' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | It consists of two convolutional layers with 64 filters each followed by a fully connected layer with 128 neurons. ... A large network, consisting of three convolutional layers with 64, 128 and 256 filters respectively followed by a fully connected layer with 512 neurons... In both models all convolutional filters are connected to a 5 5 region of their input. 2 2 maxpooling was performed after the first and second convolutional layers. Dropout [24] was applied to the fully connected layers. |