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..
Adaptive Depth Networks with Skippable Sub-Paths
Authors: Woochul Kang, HYUNGSEOP LEE
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we empirically demonstrate that our adaptive depth networks outperform counterpart individual networks, both in CNNs and vision transformers, and achieve actual inference acceleration and energy-saving. |
| Researcher Affiliation | Academia | Woochul Kang Department of Embedded Systems Incheon National University Yeonsu-gu, Incheon, South Korea, 22012 EMAIL Hyungseop Lee Department of Embedded Systems Incheon National University Yeonsu-gu, Incheon, South Korea, 22012 EMAIL |
| Pseudocode | Yes | Algorithm 1 Training algorithm for an adaptive depth network M. |
| Open Source Code | Yes | Source codes are available at https://github.com/wchkang/depth |
| Open Datasets | Yes | We evaluate our method on ILSVRC2012 dataset [49] that has 1000 classes. The dataset consists of 1.28M training and 50K validation images. |
| Dataset Splits | Yes | The dataset consists of 1.28M training and 50K validation images. |
| Hardware Specification | Yes | Table 2: Training time (1 epoch), measured on Nvidia RTX 4090 (batch size: 128). |
| Software Dependencies | No | The paper mentions training recipes from DeiT [50] and PyTorch [51] but does not provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For CNN models, we follow most training settings in the original papers [34, 35], except that Res Net models are trained for 150 epochs. Vi T and Swin-T are trained for 300 epochs, following Dei T s training recipe [50, 51]. |