*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