Hold me tight! Influence of discriminative features on deep network boundaries
Authors: Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, Pascal Frossard
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically support our findings by extensive evaluations on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Guillermo Ortiz-Jiménez EPFL, Lausanne, Switzerland guillermo.ortizjimenez@epfl.ch Apostolos Modas EPFL, Lausanne, Switzerland apostolos.modas@epfl.ch Seyed-Mohsen Moosavi-Dezfooli ETH Zürich, Zurich, Switzerland seyed.moosavi@inf.ethz.ch Pascal Frossard EPFL, Lausanne, Switzerland pascal.frossard@epfl.ch |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to reproduce our experiments can be found at https://github.com/LTS4/hold-me-tight. |
| Open Datasets | Yes | In our study, we train multiple networks on MNIST [25] and CIFAR-10 [26], and for Image Net [27] we use several of the pretrained networks provided by Py Torch [28]5. |
| Dataset Splits | No | The paper mentions training and test sets, but does not explicitly provide details about validation set splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions PyTorch [28] but does not provide specific version numbers for software dependencies. |
| Experiment Setup | No | The paper mentions general training details like using SGD and cross-entropy loss, but it does not provide specific hyperparameter values or comprehensive training configurations in the main text needed for exact reproduction. |