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..
Deep Isometric Learning for Visual Recognition
Authors: Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments with so designed simple networks on image classification using the Image Net dataset (Deng et al., 2009; Russakovsky et al., 2015). Our results show that an ISONet with more than 100 layers is trainable and can achieve surprisingly competitive performance. We evaluate the performance of R-ISONet for object detection and instance segmentation on the COCO dataset (Lin et al., 2014). 3. Experiments |
| Researcher Affiliation | Academia | Haozhi Qi 1 Chong You 1 Xiaolong Wang 1 2 Yi Ma 1 Jitendra Malik 1 1UC Berkeley 2UC San Diego. |
| Pseudocode | No | The paper describes methods and architectures but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Haozhi Qi/ISONet. |
| Open Datasets | Yes | We test the performance of ISONets on the ILSVRC2012 image classification dataset (Deng et al., 2009; Russakovsky et al., 2015). |
| Dataset Splits | Yes | The training set contains 1.28 million images from 1,000 categories. Evaluation is performed on the validation set which contains 50,000 images. |
| Hardware Specification | No | The paper mentions 'The training is performed on 8 GPUs', but does not provide specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., PyTorch 1.9, Python 3.8, CUDA 11.1). |
| Experiment Setup | Yes | All models are trained using SGD with weight decay 0.0001, momentum 0.9 and mini-batch size 256. The initial learning rate is 0.1 for R-ISONet and 0.02 for ISONet. The models are trained for 100 epochs with the learning rate subsequently divided by a factor of 10 at the 30th, 60th and 90th epochs. To stabilize training in the early stage, we perform linear scheduled warmup (Goyal et al., 2017) for the first 5 epochs for both our methods and baseline methods. The orthogonal regularization coefficient γ in (13) (when used) is 0.0001 except for ISONet-101 where a stronger regularization (0.0003) is needed. |