# Cifar10 Histopathology Eval

> Evaluates the generalization and uncertainty quantification of Bayesian Neural Networks trained with novel Jensen-Shannon divergence loss functions compared to standard KL divergence, specifically under noisy and class-biased data conditions. Use when the user wants to benchmark on CIFAR-10, Breast Histopathology Dataset, or asks about evaluating this task. Reports validation accuracy.

- Skill: `qhjqhj00/cifar10-histopathology-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/cifar10-histopathology-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/cifar10-histopathology-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/cifar10-histopathology-eval

---


# cifar10-histopathology-eval

> Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networks — Thiagarajan et al. (2022) (arXiv:2209.11366, 2022)

## What this evaluates

Evaluates the generalization and uncertainty quantification of Bayesian Neural Networks trained with novel Jensen-Shannon divergence loss functions compared to standard KL divergence, specifically under noisy and class-biased data conditions.

## Datasets

- **CIFAR-10** — total ?; splits: train (-1), val (-1), test (-1)
- **Breast Histopathology Dataset** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `validation accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly classified instances in the validation set, maximized during hyperparameter optimization.

## Input / output format

**Input**: Normalized image data (CIFAR-10) or histopathology images, optionally augmented with varying levels of Gaussian noise.

**Output**: Class predictions or probability distributions from a ResNet-18 V1 Bayesian neural network.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)
```

## Common pitfalls

- Exact noise levels added to CIFAR-10 are not specified in the text.
- Hyperparameters are tuned via TPE/Hyperopt rather than fixed, making exact reproduction difficult without the appendix.
- Batch normalization layers are explicitly removed from the ResNet-18 architecture.

## Evidence (verbatim from paper)

> An optimization is performed to maximize the validation accuracy for different hyperparameter settings of the network.

## Citation

```bibtex
@misc{thiagarajan2022jensen,
  title={Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networks},
  author={Thiagarajan et al. (2022)},
  year={2022},
  note={arXiv:2209.11366}
}
```

- arXiv: 2209.11366