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..
FLSL: Feature-level Self-supervised Learning
Authors: Qing Su, Anton Netchaev, Hai Li, Shihao Ji
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of FLSL by conducting extensive experiments. Specifically, we compare FLSL to existing SSL approaches on multiple dense prediction benchmarks: (i) MS-COCO [42] object detection and instance segmentation, (ii) UAVDT [23] object detection from UAV platforms, and (iii) DAVIS video instance segmentation [46]. Moreover, we investigate the properties of FLSL features in terms of semantic alignment and feature separability in the embedding space. |
| Researcher Affiliation | Academia | Qing Su1 , Anton Netchaev2, Hai Li3, and Shihao Ji1 1Georgia State University, 2U.S. Army ERDC, 3Duke University |
| Pseudocode | Yes | Pseudo-code, training details, and settings of augmentation pipeline are provided in Appendix E. |
| Open Source Code | Yes | The source code is available at https://github.com/ISL-CV/FLSL. |
| Open Datasets | Yes | We compare FLSL to existing SSL approaches on multiple dense prediction benchmarks: (i) MS-COCO [42] object detection and instance segmentation, (ii) UAVDT [23] object detection from UAV platforms, and (iii) DAVIS video instance segmentation [46]... Models are pretrained on Image Net-1k [52] dataset using Adam W optimizer [45] with a batch size of 512. |
| Dataset Splits | No | The paper mentions using standard schedules and training recipes but does not provide explicit training, validation, and test dataset splits with percentages, sample counts, or specific predefined split citations. |
| Hardware Specification | Yes | All our experiments are performed on Nvidia RTX A6000. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer and following DeiT for ViT implementation, but it does not provide specific version numbers for software components or libraries (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The coefficients of Eq. 13 in our experiments are υ = .03, = 1 and γ = 5 unless stated otherwise. We assume a uniform prior, i.e., k = 1/K, ∀k. Models are pretrained on Image Net-1k [52] dataset using Adam W optimizer [45] with a batch size of 512. All Vi T models are pretrained for 300 epochs as in most baselines for a fair comparison. |