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eric is a softo engineer who has been learning since the age of eight. He says he was bored with traditional software and started to learn about machine learning. In this first part of the poncast, we'll find out a little bit more about eric himself.
*Note this is an episode from Tim's Machine Learning Dojo YouTube channel.
Join Eric Craeymeersch on a wonderful discussion all about ML engineering, computer vision, siamese networks, contrastive loss, one shot learning and metric learning.
00:00:00 Introduction
00:11:47 ML Engineering Discussion
00:35:59 Intro to the main topic
00:42:13 Siamese Networks
00:48:36 Mining strategies
00:51:15 Contrastive Loss
00:57:44 Trip loss paper
01:09:35 Quad loss paper
01:25:49 Eric's Quadloss Medium Article
02:17:32 Metric learning reality check
02:21:06 Engineering discussion II
02:26:22 Outro
In our second paper review call, Tess Ferrandez covered off the FaceNet paper from Google which was a one-shot siamese network with the so called triplet loss. It was an interesting change of direction for NN architecture i.e. using a contrastive loss instead of having a fixed number of output classes. Contrastive architectures have been taking over the ML landscape recently i.e. SimCLR, MOCO, BERT.
Eric wrote an article about this at the time: https://medium.com/@crimy/one-shot-learning-siamese-networks-and-triplet-loss-with-keras-2885ed022352
He then discovered there was a new approach to one shot learning in vision using a quadruplet loss and metric learning. Eric wrote a new article and several experiments on this @ https://medium.com/@crimy/beyond-triplet-loss-one-shot-learning-experiments-with-quadruplet-loss-16671ed51290?source=friends_link&sk=bf41673664ad8a52e322380f2a456e8b
Paper details:
Beyond triplet loss: a deep quadruplet network for person re-identification
https://arxiv.org/abs/1704.01719 (Chen at al '17)
"Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method."
Original facenet paper;
https://arxiv.org/abs/1503.03832
#deeplearning #machinelearning
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