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..
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
Authors: Atsushi Yaguchi, Taiji Suzuki, Shuhei Nitta, Yukinobu Sakata, Akiyuki Tanizawa
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on the Image Net classification task, Decomposable-Net yields superior accuracy in a wide range of model sizes. We evaluate our methods on image-classification tasks of CIFAR-10/100 [Krizhevsky, 2009] and Image Net [Deng et al., 2009] datasets using deep CNNs. |
| Researcher Affiliation | Collaboration | Atsushi Yaguchi1 , Taiji Suzuki2,3 , Shuhei Nitta1 , Yukinobu Sakata1 and Akiyuki Tanizawa1 1Toshiba Corporation, Japan 2The University of Tokyo, Japan 3RIKEN Center for Advanced Intelligence Project, Japan |
| Pseudocode | Yes | The pseudo-code for learning Decomposable-Net is given in Algorithm 1. |
| Open Source Code | No | The paper provides links to third-party code used for comparison (e.g., TRP, VBMF) but does not state that the code for Decomposable-Net itself is open-source or provides a link to it. |
| Open Datasets | Yes | We evaluate our methods on image-classification tasks of CIFAR-10/100 [Krizhevsky, 2009] and Image Net [Deng et al., 2009] datasets using deep CNNs. |
| Dataset Splits | Yes | All methods are evaluated in terms of the tradeoff between validation (top-1) accuracy and the number of multiply-accumulate operations (MACs). ... we follow the same baseline setup for the CIFAR datasets as used by [Zagoruyko and Komodakis, 2016], and the setup of [Yu et al., 2019] for the Image Net dataset. |
| Hardware Specification | Yes | The wall-clock time for training Decomposable-Net with Res Net-50 on the Image Net dataset was 6.9 days with eight NVIDIA V100 GPUs |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) used for its experiments. |
| Experiment Setup | Yes | We experimentally tuned hyperparameters, and set αu = 0.25, 0.5, and 0.8, respectively for CIFAR-10, CIFAR-100, and Image Net datasets, while fixing αl = 0.01 for all datasets. Except for the results shown in Figure 2(a), we set λ = 0.5 to balance the performance of a fulland low-rank models. |