基于这些观察结果,并受 data mixing成功的推动,我们提出了hard negative mixings trategies,该策略可以以最小的计算开销实时计算。我们详尽地阐述了我们在线性分类、对象检测和实例分割方面的方法,并表明采用我们的hard negative mixing procedure 可以提高通过最先进的自监督学习方法学习的visual representations的质量。 引言...
Hard Negative Mixing for Contrastive Learning link: arxiv.org/pdf/2010.0102 我们提出了特征级的hard negative混合策略,它可以以最小的计算开销进行实时计算。 内存(灰色标记)包含许多简单的负项,而很少有困难的负项,即许多负项太远,不会导致对比损失 在本文中,我们认为对比学习的一个重要方面,即hard negative的...
Hard Negative Mixing for Constrastive Learning 标签(空格分隔): 复试 深度学习的本质:Representation Learning和Inductive Bias Learning。 目前的情况就是在不涉及逻辑推理的问题下AI系统都可以出色的完成任务,但是涉及到更高级的语义,组合逻辑,则需要涉及一些过程来辅助AI系统去分解任务。 归纳偏好的涉及更多的是任务相...
In this paper, we argue that an important aspect of contrastive learning, i.e., the effect of hard negatives, has so far been neglected. To get more meaningful negative samples, current top contrastive self-supervised learning approaches either substantially increase the batch sizes, or keep ...
MoCo (opens in new tab)(or MoCo-v2 (opens in new tab)) and SimCLR (opens in new tab)are two known studies in this line, differing in how negative samples are maintained. OpenAI’s CLIP (opens in new tab)is also based on contrastive learning, but it’s built ...
这类做法是用一个正样本,和一个负样本组成一个pair,然后线性加权,得到一个hard负样本。 mix样本 [NeurIPS-2020]《hard-negative-mixing-for-contrastive-learning-Paper》 [alibaba2021][MGDSPR] 《Embedding-based Product Retrieval in Taobao Search 》编辑...
代码地址:GitHub - joshr17/HCL: ICLR 2021, Contrastive Learning with Hard Negative Samples 先验知识: 1.hard sampling困难采样: 是一种在训练机器学习模型时使用的策略,尤其是在对比学习和度量学习等场景中。指从数据集中挑选那些对模型来说更难难以区分或分类的样本进行学习。过程通常涉及到困难负样本的选择。