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..
i-RevNet: Deep Invertible Networks
Authors: Jörn-Henrik Jacobsen, Arnold W.M. Smeulders, Edouard Oyallon
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | i-Rev Nets achieve the same performance on Imagenet compared to similar non-invertible Rev Net and Res Net architectures (Gomez et al., 2017; He et al., 2016). To shed light on the mechanism underlying the generalization-ability of the learned representation, we show that i-Rev Nets progressively separate and contract signals with depth. Our results are evidence for an effective reduction of variability through a contraction with a recoverable input obtained from a series of one-to-one mappings. |
| Researcher Affiliation | Academia | University of Amsterdam EMAIL Now at Bethgelab, University of T ubingen CVN, Centrale Sup elec, Universit e Paris-Saclay ; Galen team, INRIA Saclay Seque L team, INRIA Lille ; DI, ENS, Universit e PSL |
| Pseudocode | No | This leads to the following equations: xj+1 = Sj+1 xj xj+1 = xj + Fj+1 xj xj = S 1 j+1xj+1 xj = xj+1 Fj+1 xj (1) |
| Open Source Code | Yes | 1Code is available at: https://github.com/jhjacobsen/pytorch-i-revnet |
| Open Datasets | Yes | large-scale problems like Image Net. |
| Dataset Splits | Yes | We evaluate both classifiers for each model on the validation set of Image Net and report the Top-1 accuracy in Figure 6. |
| Hardware Specification | No | The dataset is processed for 600k iterations on a batch size of 256, distributed on 4GPUs. |
| Software Dependencies | No | 1Code is available at: https://github.com/jhjacobsen/pytorch-i-revnet |
| Experiment Setup | Yes | We train with SGD and momentum of 0.9. We regularized the model with a ℓ2 weight decay of 10 4 and batch normalization. The dataset is processed for 600k iterations on a batch size of 256, distributed on 4GPUs. The initial learning rate is 0.1, dropped by a factor of ten every 160k iterations. The images values are mapped to [0, 1] while following geometric transformations were applied: random scaling, random horizontal flipping, random cropping of size 2242, and finally color distortions. |