Compression-aware Training of Deep Networks
Authors: Jose M. Alvarez, Mathieu Salzmann
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on Image Net and ICDAR show that we can achieve compression rates of more than 90%, thus hugely reducing the number of required operations at inference time. |
| Researcher Affiliation | Collaboration | Jose M. Alvarez Toyota Research Institute Los Altos, CA 94022 jose.alvarez@tri.global Mathieu Salzmann EPFL CVLab Lausanne, Switzerland mathieu.salzmann@epfl.ch |
| Pseudocode | No | The paper describes the approach using mathematical formulations and text, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For our experiments, we used two image classification datasets: Image Net [41] and ICDAR, the character recognition dataset introduced in [27]. |
| Dataset Splits | Yes | We used the ILSVRC2012 [41] subset consisting of 1000 categories, with 1.2 million training images and 50,000 validation images. |
| Hardware Specification | Yes | For Image Net, training was done on a DGX-1 node using two-P100 GPUs in parallel. For ICDAR, we trained each network on a single Titan X-Pascal GPU for a total of 55 epochs... |
| Software Dependencies | No | More specifically, we used the torch-7 multi-gpu framework [11]. While a software name is provided, a specific version number for Torch7 is not mentioned in the text. |
| Experiment Setup | Yes | We used stochastic gradient descent with a momentum of 0.9 and a batch size of 180 images. The models were trained using an initial learning rate of 0.1 multiplied by 0.1 every 20 iterations... For ICDAR, we trained each network on a single Titan X-Pascal GPU for a total of 55 epochs with a batch size of 256 and 1,000 iterations per epoch. |