Reproducibility Index

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..

FFT-Based Dynamic Token Mixer for Vision

Authors: Yuki Tatsunami, Masato Taki

AAAI 2024 | Venue PDF | LLM Run Details

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	The results of image classification and downstream tasks, analysis, and visualization show that our models are helpful.
Researcher Affiliation	Collaboration	Yuki Tatsunami1,2, Masato Taki1 1Rikkyo University 2Any Tech Co., Ltd.
Pseudocode	No	The paper does not contain any clearly labeled pseudocode or algorithm blocks.
Open Source Code	Yes	The code is available at https://github.com/okojoalg/dfformer
Open Datasets	Yes	We conduct experiments on Image Net-1K benchmark (Krizhevsky, Sutskever, and Hinton 2012) and perform further experiments to confirm downstream tasks such as ADE20K (Zhou et al. 2017).
Dataset Splits	Yes	Image Net-1K ... contains 1,281,167 training images and 50,000 validation images.
Hardware Specification	Yes	Throughput has been benchmarked on a V100 with 16GB memory at a batch size of 16.
Software Dependencies	No	The implementation is based on Py Torch (Paszke et al. 2019) and timm (Wightman 2019). No specific version numbers for these libraries are provided.
Experiment Setup	Yes	We employ Adam W (Loshchilov and Hutter 2019) optimizer for 300 epochs with a batch size of 1024. The base learning rate of batch size 512 5 10 4, 20 epochs of linear warm-up, cosine decay for learning rate, and weight decay of 0.05 are used.