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..
Loss Function Learning for Domain Generalization by Implicit Gradient
Authors: Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the learned loss function on the four common DG benchmarks: VLCS (Fang et al., 2013), PACS (Li et al., 2018a), Office Home (Venkateswara et al., 2017), and Terra Incognita (Beery et al., 2018). |
| Researcher Affiliation | Collaboration | 1School of Informatics, University of Edinburgh 2Samsung AI Center, Cambridge. |
| Pseudocode | Yes | Further details can be found in (Lorraine et al., 2020), but we provide pseudo-code for computing the implicit gradient in Algorithm 2. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We choose Rotated MNIST (Ghifary et al., 2015) as a small dataset suitable for loss learning. We evaluate the learned loss function on the four common DG benchmarks: VLCS (Fang et al., 2013), PACS (Li et al., 2018a), Office Home (Venkateswara et al., 2017), and Terra Incognita (Beery et al., 2018). |
| Dataset Splits | Yes | We train Res Net-18 with ITL on the training split and perform model selection using the validation set. |
| Hardware Specification | Yes | training our ITL required using Py Torch (Paszke et al., 2017) only required 8 hours on a single V100 GPU to complete 200 gradient descent steps on ω. |
| Software Dependencies | No | The paper mentions using PyTorch and cites its creators, but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | The learning rates in the inner loop and outer loop are both 0.01, a batch size of 32 is used for the inner loop, and the Neumann series used for approximating the inverse Hessian is truncated at 15 iterations. |