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..
Self-Supervised Few-Shot Learning on Point Clouds
Authors: Charu Sharma, Manohar Kaul
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a comprehensive empirical evaluation of our method on both downstream classification and segmentation tasks and show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods. |
| Researcher Affiliation | Academia | Charu Sharma and Manohar Kaul Department of Computer Science & Engineering Indian Institute of Technology Hyderabad, India EMAIL, EMAIL |
| Pseudocode | No | The paper describes the network architecture and methods but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'Our code' in a footnote (footnote 3) but does not provide a specific repository link or an explicit statement of public availability for the source code. |
| Open Datasets | Yes | For self-supervised and FSL experiments, we pick two real-world datasets (Model Net40 [15] and Sydney4) for 3D shape classification and for our segmentation related experiments, we conduct part segmentation on Shape Net [24] and semantic segmentation on Stanford Large-Scale 3D Indoor Spaces (S3DIS) [25]. |
| Dataset Splits | No | The paper describes training with a 'support set S' and testing with a 'query set Q', but does not explicitly mention a 'validation' set or its specific split for reproduction purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Point Net, DGCNN, and linear SVM, but does not provide specific version numbers for any software dependencies or frameworks. |
| Experiment Setup | No | The paper describes network architecture layer sizes (e.g., MLP layers with 32, 64, 128 dimensions) and discusses the choice of the expansion constant ϵ (e.g., ϵ = 2.2). However, it lacks specific training hyperparameters such as learning rate, batch size, optimizer details, or number of epochs, which are crucial for complete reproduction. |