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..
A Label is Worth A Thousand Images in Dataset Distillation
Authors: Tian Qin, Zhiwei Deng, David Alvarez-Melis
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a series of ablation experiments, we study the role of soft labels in depth. Our results reveal that the main factor explaining the performance of state-of-the-art distillation methods is not the specific techniques used to generate synthetic data but rather the use of soft labels. |
| Researcher Affiliation | Collaboration | Tian Qin Harvard University Cambridge, MA EMAIL Zhiwei Deng Google Deep Mind Mountain View, CA EMAIL David Alvarez-Melis Harvard University & MSR Cambridge, MA EMAIL |
| Pseudocode | Yes | Algorithm 1 Learn soft label with BPTT |
| Open Source Code | Yes | Code for all experiments is available at https://github.com/sunnytqin/no-distillation. |
| Open Datasets | Yes | Table 1: Benchmark SOTA methods against Cut Mix baseline and soft label baseline on Image Net-1K. Table 2: Benchmark SOTA methods against soft label baseline ( Sl baseline") on Tiny Image Net, CIFAR-100 and CIFAR-10. |
| Dataset Splits | No | The paper does not explicitly specify validation dataset splits or how they were derived from the training data, although it does discuss expert training and hyperparameter tuning which often implies a validation set is used. It references 'standard training recipe' but does not detail the splits. |
| Hardware Specification | Yes | All experiments are conducted on NVIDIA A100 SXM4 40GB or NVIDIA H100 80GB HBM3. |
| Software Dependencies | No | The paper mentions 'Py Torch' and cites a paper [21] from 2019 about it, implying a version context from that year. However, it does not provide a specific version number (e.g., 'PyTorch 1.9') for the software dependency. |
| Experiment Setup | Yes | We follow a standard training recipe to train experts on downsized Image Net-1K, Tiny Image Net, CIFAR-10, and CIFAR-100. This standard training recipe involves an SGD optimizer and a simple step learning rate schedule... Table 7: Hyperparameter list to reproduce soft label baseline results in Table 1 and Table 2. |