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..
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Authors: ABHRA CHAUDHURI, Massimiliano Mancini, Zeynep Akata, Anjan Dutta
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design Relational Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets and achieve state-of-the-art results on all of them, surpassing the performance of all existing works with a margin exceeding 4% in some cases. We also experimentally validate our theory on fine-grained distinguishability and obtain consistent results across multiple benchmarks. |
| Researcher Affiliation | Academia | 1 University of Exeter 2 University of Tübingen 3 MPI for Informatics 4 MPI for Intelligent Systems 5 University of Surrey |
| Pseudocode | No | The paper describes its methods but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation is available at https://github.com/abhrac/relational-proxies. |
| Open Datasets | Yes | We evaluate our model on the four most common fine-grained visual categorization benchmarks (number of classes and train/test splits respectively in brackets): FGVC Aircraft [26], Stanford Cars [20], CUB [41], and NA Birds [39]. For large scale benchmark evaluation, we choose the i Naturalist 2017 dataset which consists of 13 super-categories that have been split into a total of 5089 fine-grained categories with 675,170 training and 182,707 test images. We also perform experiments on two challenging datasets of the cultivar domain that offer very low inter-class variations, namely Cotton Cultivar [46] (80 | 240/240) and Soy Cultivar [46] (200 | 600/600). |
| Dataset Splits | No | The paper provides train/test splits for the datasets but does not explicitly mention a separate validation split with specific sizes or percentages. |
| Hardware Specification | Yes | We implement our Relational Proxy model using the Py Torch [32] deep learning framework, on an Ubuntu 20.04 workstation with a single NVIDIA Ge Force RTX 3090 GPU, an 8-core Intel Xeon processor and 32 GBs of RAM. |
| Software Dependencies | No | The paper mentions using 'Py Torch [32] deep learning framework' but does not specify a version number for PyTorch or other software dependencies like Python or CUDA. |
| Experiment Setup | Yes | We use Res Net50 [14] pretrained on Image Net [9] as the backbone of our relation-agnostic encoder f. In Sec. 1.3 of the supplementary, we also provide evaluations using VGG-16 [36] to show that the performance gains achieved by our model do not depend on the specific backbone. We train our full Relational Proxy model end-to-end for 200 epochs using the stochastic gradient descent optimizer with an initial learning rate of 0.001 (decayed by a factor of 0.1 every 50 epochs), a momentum of 0.9, and a weight decay of 10 4. |