Anytime Inference with Distilled Hierarchical Neural Ensembles
Authors: Adria Ruiz, Jakob Verbeek9463-9471
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that, compared to previous anytime inference models, HNE provides state-of-the-art accuracy-computation trade-offs on the CIFAR-10/100 and Image Net datasets. |
| Researcher Affiliation | Collaboration | Adria Ruiz1, Jakob Verbeek2 1 Institut de Rob otica i Inform atica Industrial, CSIC-UPC, aruiz@iri.upc.edu 2 Facebook AI Research, jjverbeek@fb.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have released a Pytorch implementation of HNE 1. https://gitlab.com/adriaruizo/dhne-aaai21 |
| Open Datasets | Yes | We experiment with the CIFAR-10/100 (Krizhevsky 2009) and Image Net (Russakovsky et al. 2015) datasets. |
| Dataset Splits | Yes | CIFAR-10/100 contain 50k train and 10k test images from 10 and 100 classes, respectively. Following standard protocols (He et al. 2016), we pre-process the images by normalizing their mean and standard-deviation for each color channel. Additionally, during training we use a data augmentation process where we extract random crops of 32 32 after applying a 4-pixel zero padding to the original image or its horizontal flip. Imagenet is composed by 1.2M and 50k high-resolution images for training and validation, respectively, labelled across 1,000 different categories. |
| Hardware Specification | No | The paper mentions general use of GPUs but does not provide specific hardware details (e.g., specific GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch implementation' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | No | The paper states, 'In the supplementary material we present a detailed description of our HNE implementation using Res Net and Mobile Netv2 and provide all the training hyperparameters,' indicating that specific experimental setup details are not in the main text. |