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..
DISK: Learning local features with policy gradient
Authors: Michał Tyszkiewicz, Pascal Fua, Eduard Trulls
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on three different benchmarks, and present two ablation studies. |
| Researcher Affiliation | Collaboration | Michał J. Tyszkiewicz1 Pascal Fua1 Eduard Trulls2 1École Polytechnique Fédérale de Lausanne (EPFL) 2Google Research, Zurich EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Training and inference code is available at https://github.com/cvlab-epfl/disk. |
| Open Datasets | Yes | We use a subset of the Mega Depth dataset [19], from which we choose 135 scenes with 63k images in total. |
| Dataset Splits | Yes | We rely on a validation set of two scenes: Sacre Coeur and St. Peter s Square . |
| Hardware Specification | No | The paper mentions "GPU memory" but does not specify any particular GPU model, CPU, or other hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using ADAM [17] but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Rewards are λtp = 1, λfp = 0.25 and λkp = 0.001. We use a batch of two scenes, with three images in each. We use ADAM [17] with learning rate of 10 4. To pick the best checkpoint, we evaluate performance in terms of pose estimation accuracy in stereo, with DEGENSAC [7]. Specifically, every 5k optimization steps we compute the mean Average Accuracy (m AA) at a 10o error threshold, as in [16]: see Sec. 4.1 and the appendix for details. We anneal λfp and λkp over the first 5 epochs, starting with 0 and linearly increasing to their full value at the end. Grid cells are square, with each side h = 8 pixels. |