Semi-supervised learning (SSL) is one of the main approaches to address the high cost of manual annotation in supervised learning. In recent years, SSL methods have effectively utilized consistency regularization on unlabeled data to improve performance while leveraging a small portion of labeled data...
因此,目前还缺乏一种能够全面理解类别概念、有效区分类别间差异、挖掘数据中隐藏的模式和结构的开放世界SSL方法。 为此,本文提出了一种新颖的SSL方法,名为Self-Supervised Open-World Class方法,旨在显示地自学习多个未知类别。具体而言,我们首先初始化已知和未知类别的类原型(类中心)表示,然后利用交叉注意力机制结合数据...
Supervised and semi-supervised learning methods have been traditionally designed for the closed-world setting based on the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, the real world is inherently open and dynamic, and thus novel...
Semi-supervised learning (SSL) is one of the main approaches to address the high cost of manual annotation in supervised learning. In recent years, SSL methods have effectively utilized consistency regularization on unlabeled data to improve performance while leveraging a small portion of labeled data...
发现 PCHID 背后的思想已经被人做了并且称为 hindsight，但我用 supervised learning 搞 RL 仍然很 novel 的时候；在 PCL 的阳台上和丁哥打电话，厘清了 PCHID 的动态优化对应的时候；抱着随机过程的书，想明白 state-space navigation 本质上就是缩短 first hitting time 的时候；为了快速迭代，设计的简单 Maze...
发现PCHID背后的思想已经被人做了并且称为hindsight,但我用supervised learning搞RL仍然很novel的时候;在PCL的阳台上和丁哥打电话,厘清了PCHID的动态优化对应的时候;抱着随机过程的书,想明白state-space navigation 本质上就是缩短first hitting time的时候;为了快速迭代,设计的简单Maze环境在我尝试了所有SOTA算法数月...
Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned ...
《Unsupervised Learning via Meta-Learning》GitHub:O网页链接《SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition》GitHub:O网页链接《MixMatch: A Holistic Approach to Semi-Supervised Learning》GitHub:O网页链接《Cooperative Learning of Disjoint Syntax and Semantics》GitHub:O网页链接...
第二阶段:SFT(Supervised Fine-tuning)有监督微调,构造指令微调数据集,在预训练模型基础上做指令精调,以对齐指令意图 第三阶段:RM(Reward Model)奖励模型建模,构造人类偏好排序数据集,训练奖励模型,用来对齐人类偏好,主要是"HHH"原则,具体是"helpful, honest, harmless" 第四阶段:RL(Reinforcement Learning)基于人类反...
subsequently employed a semi-supervised learning approach that was trained directly on the dataset itself 44. This automatically adapts the ML model to the experimental data, and along with other MS analysis tools45,46,47,48,49, we also employ semi-supervised learning for PSM scoring in Alpha...