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..
Trading robust representations for sample complexity through self-supervised visual experience
Authors: Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental evaluation of learned image representations |
| Researcher Affiliation | Collaboration | The Center for Brains, Minds and Machines, MIT Mc Govern Institute for Brain Research at MIT Cambridge, MA, USA EMAIL; Currently with Deep Mind. Currently with X, Alphabet. |
| Pseudocode | No | The paper describes methods mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | Table 1: Datasets, orbits and canonical definitions used in our experimental evaluations. Overall, the sets include 3D viewpoint, light and unconstrained transformations; MNIST is the only one with analytic, known transformation orbits. |
| Dataset Splits | Yes | For each dataset, we employ an Embedding set to learn representations and a separate Validation set for determining the optimal number of SGD iterations (early stopping hyperparameter) by maximizing the mean, across multiple re-splits of the set, of task-specific performance metrics. The representations are used for encoding Train and Test sets from a Supervised set for the downstream recognition task. Performance is reported as the mean/std of multiple train/test re-splits of the Supervised set. For all experiments, there is no overlap between embedding, validation, and supervised sets, or between train/test splits, within the validation and the supervised sets. |
| Hardware Specification | Yes | The DGX-1 used for our experiments was donated by NVIDIA. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'VGG networks' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | Loss minimization was carried out with mini-batch Stochastic Gradient Descent using the Adam optimizer. For MNIST, we used mini-batches of size 256, for Multi-PIE, 72, for NORB, 256, for Multi-PIE, 72 and for the Late Night dataset, 128 images. The selection of triplets for ST, OT and OJ followed the soft negative selection process from [25]. The values for λ1 and λ2 were set equal (λ1 = λ2 = 1). |