add unlearned methods

src/display/about.py

@@ -18,23 +18,25 @@ We show that this dataset plays a pivotal role in establishing a standardized an
 LLM_BENCHMARKS_TEXT = f"""
 # Context
 
-## How it works
+## Evaluated MU Methods
 
-We evaluate LLMs on 10 widely recognized game-theoretic tasks, including
+We evaluate 5 most recently proposed MU methods for DMs:
+
+- <a href="https://erasing.baulab.info" target="_blank">  ESD </a>
+- <a href="https://arxiv.org/abs/2303.17591" target="_blank">  FMN </a>
+- <a href="https://unified.baulab.info" target="_blank">  UCE </a>
+- <a href="https://arxiv.org/abs/2303.13516" target="_blank">  CA </a>
+- <a href="https://arxiv.org/abs/2310.12508" target="_blank">  Salun </a>
 
-- <a href="https://en.wikipedia.org/wiki/Tic-tac-toe" target="_blank">  Tic-Tac-Toe</a>
-- <a href="https://en.wikipedia.org/wiki/Connect_Four" target="_blank">  Connect-4 </a>
-- <a href="https://en.wikipedia.org/wiki/Breakthrough_(board_game)" target="_blank">  Breakthrough</a>
-- <a href="https://en.wikipedia.org/wiki/Nim" target="_blank">  Nim</a>
-- <a href="https://en.wikipedia.org/wiki/First-price_sealed-bid_auction" target="_blank">  Blind Auction</a>
-- <a href="https://en.wikipedia.org/wiki/Kuhn_poker" target="_blank">  Kuhn Poker</a>
-- <a href="https://en.wikipedia.org/wiki/Liar\%27s_dice" target="_blank">  Liar's Dice</a>
-- <a href="https://arxiv.org/pdf/1706.05125.pdf" target="_blank">  Negotiation</a>
-- <a href="https://en.wikipedia.org/wiki/Pig_(dice_game)" target="_blank">  Pig</a>
-- <a href="https://en.wikipedia.org/wiki/Prisoner\%27s_dilemma" target="_blank">  Prisoner's Dilemma</a>
 
 ## Metrics
-- <strong> Unlearning accuracy (UA):</strong> It represents the proportion of images generated by the unlearned DM using the unlearning target-related prompt, which are not correctly classified into the corresponding class for the case.
+- Unlearning accuracy (<strong>UA</strong>): It represents the proportion of images generated by the unlearned DM using the unlearning target-related prompt, which are not correctly classified into the corresponding class for the case.
+- In-domain retain accuracy (<strong>IRA</strong>): It is given by the classification accuracy of images generated by the unlearned DM using innocent prompts (not relevant to the unlearning target) in the same domain.
+- Cross-domain retain accuracy (<strong>CRA</strong>): Similar to IRA, it is given by the classification accuracy of images generated by the unlearned DM using innocent prompts but in different domains.
+- <strong>FID</strong> score: evaluate the distribution-wise generation quality of the unlearned DM.
+- <strong>run-time</strong>
+- <strong>storage</strong> space requirements
+- <strong>memory</strong> costs
 
 
 
@@ -54,7 +56,7 @@ FAQ_TEXT = """
 
 
 EVALUATION_QUEUE_TEXT = """
-# Evaluation for the GTBench leaderboard 
+# Evaluation for the UnlearnCanvas Benchmark 
 
 """