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 [1].
Self-Supervised Generalisation with Meta Auxiliary Learning
Authors: Shikun Liu, Andrew Davison, Edward Johns
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on image classification, we show three key results. First, MAXL outperforms single-task learning across seven image datasets, without requiring any additional data. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. |
| Researcher Affiliation | Academia | Shikun Liu Andrew J. Davison Edward Johns Department of Computing, Imperial College London EMAIL |
| Pseudocode | Yes | Algorithm 1: The MAXL algorithm |
| Open Source Code | Yes | Source code can be found at https://github.com/lorenmt/maxl. |
| Open Datasets | Yes | We evaluated on seven different datasets, with varying sizes and complexities. One of these, CIFAR-100 [18]... For the other six datasets: MNIST [19], SVHN [12], CIFAR-10 [18], Image Net [7], CINIC-10 [6] and UCF-101 [32] |
| Dataset Splits | No | The paper mentions training data and test accuracy but does not specify details about a validation dataset split or its size/usage for hyperparameter tuning, only that 'We used hyper-parameter search'. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For both the primary and auxiliary tasks, we apply the focal loss [22] with a focusing parameter γ = 2, defined as: L(ˆy, y) = y(1 ˆy)γ log(ˆy),... where α is the learning rate... β is the learning rate; Entropy weighting: λ |