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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
The Reversible Residual Network: Backpropagation Without Storing Activations
Authors: Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,647 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,465 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of Rev Nets on CIFAR-10, CIFAR-100, and Image Net, establishing nearly identical classification accuracy to equally-sized Res Nets, even though the activation storage requirements are independent of depth. |
| Researcher Affiliation | Collaboration | Aidan N. Gomez 1, Mengye Ren 1,2,3, Raquel Urtasun1,2,3, Roger B. Grosse1,2 University of Toronto1 Vector Institute for Artificial Intelligence2 Uber Advanced Technologies Group3 EMAIL |
| Pseudocode | Yes | Algorithm 1 Reversible Residual Block Backprop |
| Open Source Code | Yes | Code available at https://github.com/renmengye/revnet-public |
| Open Datasets | Yes | We experimented with Rev Nets on three standard image classification benchmarks: CIFAR-10, CIFAR-100, [17] and Image Net [26]. |
| Dataset Splits | Yes | We experimented with Rev Nets on three standard image classification benchmarks: CIFAR-10, CIFAR-100, [17] and Image Net [26]. Furthermore, Figure 3 compares Image Net training curves of the Res Net and Rev Net architectures; reversibility did not lead to any noticeable per-iteration slowdown in training. (As discussed above, each Rev Net update is about 1.5-2 more expensive, depending on the implementation.) |
| Hardware Specification | Yes | For our Image Net experiments, we fixed the mini-batch size to be 256, split across 4 Titan X GPUs with data parallelism [28]. |
| Software Dependencies | No | The paper states 'We implemented the Rev Nets using the Tensor Flow library [1]' but does not specify a version number for TensorFlow or any other software dependency. |
| Experiment Setup | Yes | For our CIFAR-10/100 experiments, we fixed the mini-batch size to be 100. The learning rate was initialized to 0.1 and decayed by a factor of 10 at 40K and 60K training steps, training for a total of 80K steps. The weight decay constant was set to 2 10 4 and the momentum was set to 0.9. For our Image Net experiments, we fixed the mini-batch size to be 256, split across 4 Titan X GPUs with data parallelism [28]. We employed synchronous SGD [4] with momentum of 0.9. The model was trained for 600K steps, with factor-of-10 learning rate decays scheduled at 160K, 320K, and 480K steps. Weight decay was set to 1 10 4. |