Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Automatic Discovery and Optimization of Parts for Image Classification
Authors: Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, and Pedro Felzenszwalb
ICLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments with both HOG (Dalal & Triggs (2005)) and CNN (Krizhevsky et al. (2012)) features and improve the state-of-the-art results on the MIT-indoor dataset (Quattoni & Torralba (2009)) using CNN features. |
| Researcher Affiliation | Academia | Brown University University of Oxford Brown University |
| Pseudocode | Yes | Algorithm 1 Joint training of model parameters by optimizing O(u, w) in Equation 6. Algorithm 2 Fast optimization of the convex bound Bu(w, wold) using hard example mining. Algorithm 3 Fast QP solver for optimizing BC. |
| Open Source Code | No | The paper mentions using a third-party tool, Caffe, but it does not provide an explicit statement about releasing its own source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our methods on the MIT-indoor dataset (Quattoni & Torralba (2009)). The hybrid network is pre-trained on images from Image Net (Deng et al. (2009)) and PLACES (Zhou et al. (2014)) datasets. |
| Dataset Splits | No | The paper states: "The dataset has 67 indoor scene classes. There are about 80 training and 20 test images per class." While it mentions training and test sets, it does not specify a separate validation split or explicit percentages/counts for data partitioning, nor does it refer to predefined splits with citations. |
| Hardware Specification | Yes | In our current implementation it takes 5 days to do joint training with 120 shared parts on the full MIT-indoor dataset on a 16-core machine using HOG features. It takes 2.5 days to do joint training with 372 parts on the full dataset on a 8 core machine using 60-dimensional PCA-reduced CNN features. |
| Software Dependencies | No | The paper states: "We extract CNN features using Caffe (Jia et al. (2014))." It mentions Caffe, but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | HOG features: We resize images (maintaining aspect ratio) to have about 2.5M pixels. We extract 32-dimensional HOG features... at multiple scales. Our HOG pyramid has 3 scales per octave... Each part ๏ฌlter wj models a 6 6 grid of HOG features... CNN features: We extract CNN features at multiple scales from overlapping patches of ๏ฌxed size 256 256 and with stride value 256/3 = 85. We resize images (maintaining aspect ratio) to have about 5M pixels in the largest scale. We use a scale pyramid with 2 scales per octave. |