Daesoo Lee's Blog

Reference: www.jeremyjordan.me/variational-autoencoders/ Variational autoencoders. In my introductory post on autoencoders, I discussed various models (undercomplete, sparse, denoising, contractive) which take data as input and discover some latent state representation of that data. More specifically, our input data is converted into an www.jeremyjordan.me Key Concepts We define $x$, $z$ as inpu..

Cosine-similarity Classifier introduced in [S. Gidaris et al., 2018] is implemented here. The cosine-similarity classifier is compared to the linear-softmax classifier. The codes can be found in my Github. [W. Chen et al., 2020] verifies the performance improvement by the cosine-similarity classifier in the few-shot learning regime. Result; In the one-shot learning regime The models are trained ..

S. Gidaris et al., 2018, "Dynamic few-shot visual learning without forgetting" This paper proposes a few-shot object recognition system that is capable of dynamically learning novel categories from only a few training data while at the same time does not forget the base categories on which it was trained. To achieve that, the authors introduced the following two: 1) Classifier of a ConvNet as a ..

[Seminar Video] Metric-based Approaches to Meta-learning Common Terminology regarding a Dataset for the Meta-learning What is a Metric-based Approach to Meta-learning? Metric-learning의 개념을 이용해서 meta-learning에 적용시킨 그런 방법론들을 지칭: Deep siamese network; Matching network; Protypical network; Relation network; Reference: http://dmqm.korea.ac.kr/activity/seminar/301 G. Koch, et al., 2015, "Siamese Neura..