Core Viewpoint - A new paradigm in supervised learning called Predictive Consistency Learning (PCL) is introduced, which redefines the role of labels as auxiliary references rather than just standard answers for comparison [1][5]. Group 1: Training Process Overview - PCL aims to capture complex label representations by progressively decomposing label information, allowing the model to predict complete labels with partial label hints [5][6]. - The training process involves mapping noisy labels back to true labels, with noise levels controlled by time steps, ensuring predictions remain consistent across different noise levels [7][8]. Group 2: Noise Process - The noise process for discrete labels is modeled using a categorical distribution, while continuous labels follow a Gaussian diffusion model, introducing noise progressively [9][11]. - In cases where labels are too complex, PCL introduces Gaussian noise directly into the latent embedding space, aligning with the continuous label noise process [11]. Group 3: Testing Process Overview - After training, the model can efficiently predict by sampling from a random noise distribution, achieving results that surpass traditional supervised learning even without label hints [14][28]. - A multi-step inference strategy is employed to refine predictions, where previous predictions are perturbed with noise to serve as hints for subsequent predictions [14][28]. Group 4: Information Theory Perspective - PCL proposes a structured learning process that gradually captures information, allowing the model to learn from noisy labels while minimizing dependency on them [15][18]. - The model's goal is to minimize noise condition dependence, ensuring predictions remain consistent across varying noise levels [19]. Group 5: Experimental Results - PCL demonstrates significant improvements in prediction accuracy across various tasks, including image segmentation, graph-based predictions, and language modeling, compared to traditional supervised learning [20][25][30]. - In image segmentation, PCL outperforms traditional methods in single-step predictions and continues to improve with additional prediction steps [22][28]. - The results indicate that while more inference steps can enhance detail capture, they also risk error accumulation, necessitating a balance in the number of steps [26][28].

清华交叉信息研究院硕士,研究方向为多模态生成,扩散模型,一致性模型 代表工作有 LCM, LCM-LoRA, Diff-Foley · 王浩帆 硕士毕业于 CMU,InstantX 团队成员,研究方向为一致性生成 代表工作有 InstantStyle, InstantID 和 Score-CAM · 杨策元 42章经 AI 私董会活动 文生图与文生视频 从研究到应用 分享嘉宾 · 骆思勉 LCM、InstantID 和 AnimateDiff 这三个研究在全球的意义和影响力都非常之大，可以说是过去一整年里给文生图和文生视频相关领域带来极大突破或应用 落地性的工作，相信有非常多的创业者都在实际使用这些作品的结果。 这次，我们首次把这三个工作的作者凑齐，并且还请来了知名的 AI 产品经理 Hidecloud 做 Panel 主持，届时期待和数十位 AI 创业者一起交流下文生图、文生视频 领域最新的研究和落地。 PhD 毕业于香港中文大学,研究方向为视频生成 6/01 | 13:00-14:00 (周六) 北京时间 美西时间 5/31 | 22:00-23:00 (周五) 活动形式 线上(会议链接将一对一发送) ...