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..
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
Authors: Thomas Yerxa, Yilun Kuang, Eero Simoncelli, SueYeon Chung
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we simplify this measure to a form that facilitates direct optimization, use it to learn Maximum Manifold Capacity Representations (MMCRs), and demonstrate that these are competitive with state-of-the-art results on current self-supervised learning (SSL) recognition benchmarks. Empirical analyses reveal important differences between MMCRs and the representations learned by other SSL frameworks, and suggest a mechanism by which manifold compression gives rise to class separability. Finally, we evaluate a set of SSL methods on a suite of neural predictivity benchmarks, and find MMCRs are highly competitive as models of the primate ventral stream. |
| Researcher Affiliation | Collaboration | Thomas Yerxa 1 Yilun Kuang 2,3 Eero Simoncelli 1,2,3 Sue Yeon Chung1,2 1Center for Neural Science, New York University 2Center for Computational Neuroscience, Flatiron Institute 3Courant Inst. of Mathematical Sciences, EMAIL |
| Pseudocode | Yes | B Pytorch Style Pseudocode for MMCR |
| Open Source Code | No | The paper references open-source code for baseline methods (e.g., Mo Co, Barlow Twins, BYOL from solo-learn; SwAV, SimCLR from VISSL), but does not provide a link or explicit statement for the open-sourcing of their own MMCR implementation. |
| Open Datasets | Yes | Table 1: Evaluation of learned features on downstream classification tasks... Image Net (IN)...Food-101 Flowers-102 DTD. Table 4: Top-1 classification accuracies of linear classifiers for representations trained with various datasets... CIFAR-10 CIFAR-100 STL-10 Image Net-100... Appendix M: ...fine tuning the representation network with a Faster R-CNN head and C-4 backbone on the VOC07+12 dataset... |
| Dataset Splits | Yes | Columns 2 and 3 show semi-supervised evaluation on Image Net (fine-tuning on 1% and 10% of labels). ... We also perform semi-supervised evaluation, where all model parameters are fine tuned using a small number of labelled examples... |
| Hardware Specification | Yes | Pre-training on 16 A100 GPUs using 8 views (our most compute intensive setting) takes approximately 32 hours. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer (46)', 'LARS optimizer', 'SGD optimizer', 'detectron2 library', and implies 'PyTorch' through pseudocode, but it does not specify version numbers for these dependencies. |
| Experiment Setup | Yes | For Image Net we used the LARS optimizer with a learning rate of 4.8, linear warmup during the first 10 epochs and cosine decay thereafter with a batchsize of 2048, and pre-train for 100 epochs. ... For smaller CIFAR-10 we used a smaller batch size, many more views (40), and the Adam optimizer with fixed learning rate. ...All models were trained for 500 epochs using the Adam optimizer (46) with a learning rate of 1e-3 and weight decay of 1e-6. |