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..
Investigating the Benefits of Projection Head for Representation Learning
Authors: Yihao Xue, Eric Gan, Jiayi Ni, Siddharth Joshi, Baharan Mirzasoleiman
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our results through various experiments on CIFAR-10/100, Urban Cars and shifted versions of Image Net. We also introduce a potential alternative to projection head, which offers a more interpretable and controllable design. |
| Researcher Affiliation | Academia | Department of Computer Science, University of California, Los Angeles |
| Pseudocode | No | The paper contains theoretical analyses and experimental results but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of its source code. |
| Open Datasets | Yes | We empirically validate our results through various experiments on CIFAR-10/100, Urban Cars and shifted versions of Image Net. |
| Dataset Splits | No | The paper discusses training and evaluation but does not specify exact train/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'Sim CLR loss' and 'Adam optimizer' but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | By default, we use a temperature of 0.5 and minimize the Sim CLR loss with the Adam optimizer. Our training batch size is 512, with a learning rate of 0.001 and weight decay set to 1 10 6. We train for 400 epochs. |