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..
Do better ImageNet classifiers assess perceptual similarity better?
Authors: Manoj Kumar, Neil Houlsby, Nal Kalchbrenner, Ekin Dogus Cubuk
TMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present a large-scale empirical study to assess how well Image Net classifiers perform on perceptual similarity. First, we observe a inverse correlation between Image Net accuracy and Perceptual Scores of modern networks such as Res Nets, Efficient Nets, and Vision Transformers: that is better classifiers achieve worse Perceptual Scores. Then, we examine the Image Net accuracy/Perceptual Score relationship on varying the depth, width, number of training steps, weight decay, label smoothing, and dropout. |
| Researcher Affiliation | Industry | Manoj Kumar EMAIL Neil Houlsby EMAIL Nal Kalchbrenner EMAIL Ekin D. Cubuk EMAIL Google Research, Brain Team |
| Pseudocode | Yes | L Code Snippets We present code snippets for the different distance functions used in our paper. Listing 1: Code Snippets for different perceptual functions def perceptual ( tensor1 , tensor2 , eps=1e 10): """ Default perceptual distance function . |
| Open Source Code | Yes | L Code Snippets We present code snippets for the different distance functions used in our paper. Listing 1: Code Snippets for different perceptual functions def perceptual ( tensor1 , tensor2 , eps=1e 10): """ Default perceptual distance function . |
| Open Datasets | Yes | We perform a suite of experiments on BAPPS, a large dataset of human-evaluated perceptual judgements (Zhang et al., 2018). ... The BAPPS Dataset (Zhang et al., 2018) is a dataset of 161k patches derived by applying exclusively lowlevel distortions to the MIT-Adobe 5k dataset (Bychkovsky et al., 2011) for training and the RAISE1k dataset (Dang-Nguyen et al., 2015) for validation. ... Image Net (Russakovsky et al., 2015) is the cornerstone of modern supervised learning... |
| Dataset Splits | Yes | The BAPPS Dataset (Zhang et al., 2018) is a dataset of 161k patches derived by applying exclusively lowlevel distortions to the MIT-Adobe 5k dataset (Bychkovsky et al., 2011) for training and the RAISE1k dataset (Dang-Nguyen et al., 2015) for validation. ... The train set consists of the traditional and CNN-based distortions and the validation set contains all 6 families. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper provides code snippets using 'numpy' but does not specify version numbers for any software dependencies, libraries, or frameworks used for the experiments. |
| Experiment Setup | Yes | Appendix M Default Hyper-parameters We provide the default training hyper-parameters for the Res Nets, Efficient Nets and Vision Transformers in Tables 2, 3, 4 and 5. Table 2: Res Net: Default Hyperparameters (Batch Size 1024, Base Learning Rate 0.1, Train Steps 112590, Momentum 0.9, Weight Decay 0.0001, Label Smoothing 0.0, LR Schedule Step-wise Decay, Batch-Norm Momentum 0.9). Similar tables are provided for EfficientNet, ViT-B/8, and ViT-L/4. |