推特吵架吵出篇论文，谢赛宁团队新作iREPA只要3行代码

Core Insights - The online debate initiated by a Twitter user led to the development of a complete academic paper, demonstrating the potential of collaborative discussions in academia [2][4][15]. Group 1: Academic Discussion and Collaboration - The initial discussion emphasized the need for self-supervised learning (SSL) models to focus on dense tasks rather than solely on classification scores from ImageNet-1K [4]. - The debate involved various participants, including a notable contribution from a user who suggested a comparative analysis between different models [11]. - The outcome of the discussion was a paper that provided deeper insights into the relationship between representation quality and generative performance [15]. Group 2: Research Findings - The paper concluded that spatial structure, rather than global semantic information, is the primary driver of generative performance in models [18]. - It was found that larger visual encoders do not necessarily lead to better generation results; in fact, encoders with lower accuracy could outperform those with higher accuracy [18][21]. - The research highlighted the importance of spatial information, showing that even classic spatial features like SIFT and HOG can provide competitive improvements [22]. Group 3: Methodological Innovations - The study proposed modifications to the existing representation alignment framework (REPA), introducing iREPA, which enhances spatial structure retention [24]. - Simple changes, such as replacing the standard MLP projection layer with a convolutional layer, were shown to significantly improve performance [25]. - iREPA can be easily integrated into various representation alignment methods with minimal code, facilitating faster convergence across different training schemes [25].