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..
Efficient Heterogeneous Meta-Learning via Channel Shuffling Modulation
Authors: Minh Hoang, Carl Kingsford
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed methods on two public datasets, namely Mini-ImageNet and Tiered-ImageNet [35, 34]. For Mini-ImageNet, each image is of size 84 × 84. It contains 100 classes with 600 images per class. Following the common setting [3, 23], we divide Mini-ImageNet into 64, 16, and 20 classes for meta-training, meta-validation, and meta-testing, respectively. For Tiered-ImageNet, each image is of size 84 × 84. It contains 608 classes which are divided into 34, 10, and 16 classes for meta-training, meta-validation, and meta-testing, respectively. We follow the standard few-shot classification setting for all experiments. In particular, we evaluate our method on the 5-way 1-shot/5-shot settings. For a fair comparison, we repeat each experiment 3 times using different random seeds and report the average performance. |
| Researcher Affiliation | Academia | Hanyang University, Korea |
| Pseudocode | No | The paper describes the method algorithmically but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide a statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our proposed methods on two public datasets, namely Mini-ImageNet and Tiered-ImageNet [35, 34]. For Mini-ImageNet, each image is of size 84 × 84. It contains 100 classes with 600 images per class. Following the common setting [3, 23], we divide Mini-ImageNet into 64, 16, and 20 classes for meta-training, meta-validation, and meta-testing, respectively. For Tiered-ImageNet, each image is of size 84 × 84. It contains 608 classes which are divided into 34, 10, and 16 classes for meta-training, meta-validation, and meta-testing, respectively. |
| Dataset Splits | Yes | For Mini-ImageNet, each image is of size 84 × 84. It contains 100 classes with 600 images per class. Following the common setting [3, 23], we divide Mini-ImageNet into 64, 16, and 20 classes for meta-training, meta-validation, and meta-testing, respectively. For Tiered-ImageNet, each image is of size 84 × 84. It contains 608 classes which are divided into 34, 10, and 16 classes for meta-training, meta-validation, and meta-testing, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | For all few-shot classification experiments, we use ResNet-18 [10] as the backbone. In the meta-training stage, we use stochastic gradient descent (SGD) as the optimizer with a learning rate of 0.001. We train our model for 100 epochs, and the learning rate is decayed by a factor of 0.1 at 80 epochs. We use a batch size of 256. For data augmentation, we use random horizontal flip, random crop, and color jittering. In the meta-testing stage, for fair comparison, we use the same evaluation protocol as [23, 24]. In particular, we randomly sample 600 episodes for each setting and report the average accuracy and 95% confidence interval. |