Update README.md

README.md

@@ -7,6 +7,81 @@ sdk: static
 pinned: false
 license: mit
 short_description: Machine Unlearning Visual Playground
+thumbnail: img/teaser.gif  # 썸네일을 위해 이 줄을 추가합니다. (2번 항목 참고)
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧹 Machine Unlearning Comparator
+*Under review*  
+
+![Teaser Animation](https://raw.githubusercontent.com/gnueaj/machine-unlearning-comparator/main/img/teaser.gif)
+
+Machine Unlearning Comparator is a **web-based visual-analytics toolkit** for **seeing, testing, and comparing** how unlearning methods balance the three MU principles—**accuracy, efficiency, and privacy**—from class- to layer-level detail.
+
+![Unlearning Comparator Workflow](https://raw.githubusercontent.com/[사용자명]/[저장소명]/main/img/fig_workflow_github.png)
+
+---
+
+## 🔗 Demo & Video
+
+- **Live demo** → [Machine Unlearning Comparator](https://gnueaj.github.io/Machine-Unlearning-Comparator/)  
+- **5-min overview** → [Youtube](https://youtu.be/yAyAYp2msDk?si=Q-8IgVlrk8uSBceu)
+  
+---
+
+## 🚀 Why use the Comparator?
+
+| Pain Point | How the Comparator helps |
+|------------|--------------------------|
+| Fragmented evaluations | **One workflow — Build → Screen → Contrast → Attack** — keeps every run tidy and repeatable. |
+| Raw numbers hide behavior | Combine metrics & visuals: **Class-wise Accuracy chart, Prediction Matrix, Embedding Space, Layer-wise Similarity chart**. |
+| "Did it really forget?" | Built-in **membership-inference attacks** and an aggregated **privacy score** reveal lingering signals. |
+| Baselines vary by paper | Compare against **standard baselines** or plug in your own method via two Python hooks. |
+
+---
+
+## ✨ Key Features
+
+### Multi-Level Comparison
+* **Metrics View** – follow Unlearning/Retaining Accuracy, Run Time (RT), and the worst-case privacy score in one glance.  
+* **Embedding Space** – twin UMAPs show how feature clusters shift after unlearning.  
+* **Layer-wise Similarity** – CKA curves pinpoint layers that still encode the forget class.  
+* **Attack Simulation** – sweep thresholds, flag high-risk samples, and inspect logits interactively.
+
+### Built-in Baselines
+| Method | Idea (aligned with the paper) |
+|--------|------------------------------|
+| **Fine-Tuning (FT)** | Continue training on the **retain set** only, leveraging catastrophic forgetting of the forget set. |
+| **Gradient Ascent (GA)** | Update weights to **maximize loss** on the forget set, actively "unteaching" it. |
+| **Random Labeling (RL)** | Assign **random labels** to the forget set then fine-tune, so the model treats those samples as noise. |
+| **SCRUB** | Use **knowledge distillation** with selective forgetting, preserving important parameters while removing forget class information. |
+| **SalUn** | Apply **gradient saliency masking** to selectively update weights based on their importance for the forget class. |
+
+### Custom Method API
+Add your algorithm, register it, and the UI will automatically expose metrics, embeddings, and privacy attacks.
+
+---
+
+## ⚡ Quick Start
+
+### Backend
+\`\`\`bash
+# 1 Install deps & enter env
+hatch shell
+# 2 Run the API
+hatch run start
+\`\`\`
+
+### Frontend
+\`\`\`bash
+# 1 Install deps
+pnpm install
+# 2 Launch the UI
+pnpm start
+\`\`\`
+
+---
+
+## Related Resources
+- **ResNet-18 CIFAR-10 MU checkpoints** → <https://huggingface.co/jaeunglee/resnet18-cifar10-unlearning>
+- **ResNet-18 FashionMNIST MU checkpoints** → <https://huggingface.co/Yurim0507/resnet18-fashionmnist-unlearning>
+- **ViT-Base CIFAR-10 MU checkpoints** → <https://huggingface.co/Yurim0507/vit-base-16-cifar10-unlearning>