Cifar100 Miniimagenet Eval

---
name: cifar100-miniimagenet-eval
description: This evaluation protocol probes a model's ability to perform standard supervised image classification and adaptive few-shot episodic learning. It measures how well the model generalizes to unseen classes under limited supervision by averaging accuracy over multiple sampled episodes. Use when the user wants to benchmark on CIFAR-100, Mini-ImageNet, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.23641
  bibtex_key: yan2026vdlfnet
  confidence: high
---

# cifar100-miniimagenet-eval

> VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning — Yan (2026) (arXiv:2604.23641, 2026)

## What this evaluates

This evaluation protocol probes a model's ability to perform standard supervised image classification and adaptive few-shot episodic learning. It measures how well the model generalizes to unseen classes under limited supervision by averaging accuracy over multiple sampled episodes.

## Datasets

- **CIFAR-100** — total 60000; splits: train/test (-1)
- **Mini-ImageNet** — total 60000; splits: train (38400), val (9600), test (12000)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - For standard classification, accuracy is the fraction of correctly predicted images out of the total test set. For few-shot learning, it is the mean accuracy across 100 test episodes, where each episode's accuracy is the fraction of correctly classified query images. 95% confidence intervals are computed using a normal approximation: ±1.96 * σ / √100.
- `macro precision/recall/F1` — range: [0, 1]
  - Standard macro-averaged precision, recall, and F1-score computed across all 100 classes for the CIFAR-100 supervised task.

## Input / output format

**Input**: RGB images (32×32 for CIFAR-100; standard resolution for Mini-ImageNet). For few-shot episodes, inputs are provided as a support set (N×K images) and a query set (N×Q images).

**Output**: Predicted class label or probability distribution over the N classes for each input image.

## Scoring recipe

```python
# Standard classification
correct = sum(pred == gold for pred, gold in zip(predictions, golds))
accuracy = correct / len(golds)

# Few-shot episodic
episode_accuracies = []
for episode in test_episodes:
    q_correct = sum(pred == gold for pred, gold in zip(episode.query_preds, episode.query_golds))
    episode_accuracies.append(q_correct / len(episode.query_golds))
mean_acc = sum(episode_accuracies) / len(episode_accuracies)
ci = 1.96 * np.std(episode_accuracies) / np.sqrt(len(episode_accuracies))
```

## Common pitfalls

- Using data augmentation (e.g., random crops/flips) during evaluation instead of the specified center crop.
- Reporting single-run few-shot accuracy without averaging over 100 test episodes and computing the 95% confidence interval.
- Confusing the standard supervised train/test split with the 64/16/20 episodic split used for Mini-ImageNet.

## Evidence (verbatim from paper)

> CIFAR-100: accuracy and macro precision/recall/F1. Few-shot: episode mean accuracy with normal-approximation 95% intervals (100 test episodes).

## Citation

```bibtex
@misc{yan2026vdlfnet,
  title={VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning},
  author={Yan (2026)},
  year={2026},
  note={arXiv:2604.23641}
}
```

- arXiv: 2604.23641