Large Margin Deep Networks for Classification
Authors: Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the decision boundary obtained by our loss has nice properties compared to standard classification loss functions. Specifically, we show improved empirical results on the MNIST, CIFAR-10 and Image Net datasets on multiple tasks: generalization from small training sets, corrupted labels, and robustness against adversarial perturbations. |
| Researcher Affiliation | Industry | Gamaleldin F. Elsayed Google Research Dilip Krishnan Google Research Hossein Mobahi Google Research Kevin Regan Google Research Samy Bengio Google Research |
| Pseudocode | No | The paper presents mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for the large margin loss function is released at https://github.com/google-research/ google-research/tree/master/large_margin |
| Open Datasets | Yes | Specifically, we show improved empirical results on the MNIST, CIFAR-10 and Image Net datasets on multiple tasks |
| Dataset Splits | Yes | We hold out 5, 000 samples of the training set as a validation set, and the remaining 55, 000 samples are used for training. |
| Hardware Specification | Yes | measured on the same NVIDIA p100 GPU |
| Software Dependencies | No | Our code was written in Tensorflow (Abadi et al., 2016). (No version number provided for TensorFlow or other software components.) |
| Experiment Setup | Yes | We train a 4 hidden-layer model with 2 convolutional layers and 2 fully connected layers, with rectified linear unit (Re Lu) activation functions, and a softmax output layer. The first baseline model uses a cross-entropy loss function, trained with stochastic gradient descent optimization with momentum and learning rate decay. |