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..
Efficiency for Free: Ideal Data Are Transportable Representations
Authors: PENG SUN, Yi Jiang, Tao Lin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across four widely-used datasets, seven neural network architectures, eight self-supervised learning algorithms demonstrate the effectiveness and efficiency of RELA. |
| Researcher Affiliation | Academia | Peng Sun1,2 Yi Jiang1 Tao Lin2, 1Zhejiang University 2Westlake University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Loss Weighting Algorithm for RELA and Self-Supervised Learning |
| Open Source Code | Yes | Our code is available at: https://github.com/LINs-lab/Re LA. |
| Open Datasets | Yes | Datasets: For low-resolution data (32x32), we evaluate our method on two datasets, i.e., CIFAR-10 [35] and CIFAR-100 [34]. For high-resolution data, we conduct experiments on two large-scale datasets including Tiny-Image Net (64x64) [36] and full Image Net-1K (224x224) [20] |
| Dataset Splits | No | The paper refers to 'Validation Loss' in figures, implying a validation set is used, but does not explicitly provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | We implement our method through Py Torch [48], and all experiments are conducted on NVIDIA RTX 4090 GPUs. |
| Software Dependencies | No | The paper mentions 'PyTorch [48]' but does not provide a specific version number for this or any other software library. |
| Experiment Setup | Yes | This includes using Adam W as the optimizer, with a mini-batch size of 128 (except for Image Net-1K, where we use a mini-batch size of 512). ... Optimizer Adam W, Learning Rate 0.001, Weight Decay 0.01. ... Optimizer Adam (learning rate: 3e-4) |