On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Authors: Markus Hiller, Mehrtash Harandi, Tom Drummond
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive evaluations show that the proposed method significantly outperforms its unconstrained counterpart especially during initial adaptation steps, while achieving comparable or better overall results on several few-shot classification tasks creating the possibility of dynamically choosing the number of adaptation steps at inference time. |
| Researcher Affiliation | Academia | Markus Hiller1 Mehrtash Harandi2 Tom Drummond1 1School of Computing and Information Systems, The University of Melbourne 2Department of Electrical and Computer Systems Engineering, Monash University markus.hiller@student.unimelb.edu.au mehrtash.harandi@monash.edu tom.drummond@unimelb.edu.au |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | Code to be released upon acceptance |
| Open Datasets | Yes | We evaluate our method on all five popular few-shot classification datasets, namely mini Image Net (Vinyals et al., 2016; Ravi & Larochelle, 2017), tiered Image Net (Ren et al., 2018), CIFAR-FS (Bertinetto et al., 2019), FC100 (Oreshkin et al., 2018) and CUB-200-2011 (Wah et al., 2011), hereafter referred to as CUB . |
| Dataset Splits | Yes | Let Dtrain τ and Dval τ be the training and validation set of some given task τ ∼ p(T ) (e.g. image classification), respectively. and We follow previous work like Chen et al. (2019) and report the averaged test accuracies of 600 randomly sampled experiments using the k samples of the respective k-shot setting as support and 16 query samples for each class. and best model picked based on highest validation accuracy. |
| Hardware Specification | Yes | Our baselines and methods are trained on a single NVIDIA RTX 3090 for 60,000 episodes for 1-shot and 40,000 for 5-shot tasks... |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.y) are explicitly stated in the paper. |
| Experiment Setup | Yes | Our baselines and methods are trained on a single NVIDIA RTX 3090 for 60,000 episodes for 1-shot and 40,000 for 5-shot tasks, with the best model picked based on highest validation accuracy. We use 5 inner-loop updates and γ = 1. |