Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.
🌀 Abstract: This paper tackles a critical issue in text-to-image diffusion models like Stable Diffusion, DALL·E, and Midjourney. These models are trained on massive datasets, often containing private or copyrighted content, which raises serious legal and ethical concerns. To address this, machine unlearning methods have emerged, aiming to remove specific information from the models. However, this paper reveals a major flaw: these unlearned concepts can come back when the model is fine-tuned. The authors introduce a new framework to analyze and evaluate the stability of current unlearning techniques and offer insights into why they often fail, paving the way for more robust future methods.
Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.
🌀 Abstract: This paper tackles a critical issue in text-to-image diffusion models like Stable Diffusion, DALL·E, and Midjourney. These models are trained on massive datasets, often containing private or copyrighted content, which raises serious legal and ethical concerns. To address this, machine unlearning methods have emerged, aiming to remove specific information from the models. However, this paper reveals a major flaw: these unlearned concepts can come back when the model is fine-tuned. The authors introduce a new framework to analyze and evaluate the stability of current unlearning techniques and offer insights into why they often fail, paving the way for more robust future methods.
Telegram hopes to raise $1bn with a convertible bond private placement
The super secure UAE-based Telegram messenger service, developed by Russian-born software icon Pavel Durov, is looking to raise $1bn through a bond placement to a limited number of investors from Russia, Europe, Asia and the Middle East, the Kommersant daily reported citing unnamed sources on February 18, 2021.The issue reportedly comprises exchange bonds that could be converted into equity in the messaging service that is currently 100% owned by Durov and his brother Nikolai.Kommersant reports that the price of the conversion would be at a 10% discount to a potential IPO should it happen within five years.The minimum bond placement is said to be set at $50mn, but could be lowered to $10mn. Five-year bonds could carry an annual coupon of 7-8%.
The global forecast for the Asian markets is murky following recent volatility, with crude oil prices providing support in what has been an otherwise tough month. The European markets were down and the U.S. bourses were mixed and flat and the Asian markets figure to split the difference.The TSE finished modestly lower on Friday following losses from the financial shares and property stocks.For the day, the index sank 15.09 points or 0.49 percent to finish at 3,061.35 after trading between 3,057.84 and 3,089.78. Volume was 1.39 billion shares worth 1.30 billion Singapore dollars. There were 285 decliners and 184 gainers.
