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..
Representation Learning via Consistent Assignment of Views over Random Partitions
Authors: Thalles Santos Silva, Adín Ramírez Rivera
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an extensive evaluation, we demonstrate that CARP s representations are suitable for learning downstream tasks. We evaluate CARP s representations capabilities in 17 datasets across many standard protocols, including linear evaluation, few-shot classification, k-NN, kmeans, image retrieval, and copy detection. We compare CARP performance to 11 existing self-supervised methods. We extensively ablate our method and demonstrate that our proposed random partition pretext task improves the quality of the learned representations by devising multiple random classification tasks. |
| Researcher Affiliation | Academia | Thalles Silva Institute of Computing University of Campinas EMAIL Adín Ramírez Rivera Department of Informatics University of Oslo EMAIL |
| Pseudocode | Yes | E Pseudocode of CARP in a Py Torch-like Style |
| Open Source Code | Yes | Code at https://sthalles.github.io/carp/. |
| Open Datasets | Yes | Table 2 reports clustering performance metrics of various clustering-based SSL methods on the Image Net-1M [36], CIFAR-10/100 [27], and the GTSRB [40] datasets. |
| Dataset Splits | Yes | For Image Net-1M evaluation, we trained a linear classifier on top of the frozen representations extracted from the last average pooling layer of the Res Net50 encoder for 100 epochs, following Zhou et al. s [49] protocol. ... We use the validation split to assess the quality of the learned prototypes. |
| Hardware Specification | Yes | For all experiments, we used 4 A100 40GB GPUs and gradient accumulation to simulate large batch sizes. |
| Software Dependencies | No | The paper mentions 'Py Torch style pseudo-code' and 'faiss library [25]', but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train CARP on the Image Net-1M unlabeled dataset using Res Net50 [23] encoders. We take the output representation of the last global average pooling layer (a 2048-dim vector) and project it to a 256-dim vector. ... The hidden units of the projection head contain 2048 neurons. ... K = 65 536 prototypes. ... NP = 128, which creates subsets containing NB = 512 randomly chosen prototypes. ... CARP is pre-trained with the LARS [47] optimizer, end to end, with weight decay of 1 10 6. For models training up to 200 epochs, the learning rate starts from 0.6 and decays to 0.006 with a cosine scheduling [30] without warmups. For models pre-trained for more than 400 epochs, the learning rate starts at 0.3 and decays to 0.003 using the same cosine scheduler. We train the system with a global batch size of 4096 observations. |