-
Notifications
You must be signed in to change notification settings - Fork 33
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
71f2d94
commit b767f19
Showing
1 changed file
with
34 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,34 @@ | ||
# :cactus: Siamese Networks | ||
|
||
### :books: Papers | ||
|
||
[Signature Verification using a "Siamese" Time Delay Neural Network](https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf) | ||
|
||
[Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) | ||
|
||
[Facenet paper introducing Triplets](https://arxiv.org/abs/1503.03832) | ||
|
||
[Contrastive Loss paper](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) | ||
|
||
[In defense of Triplet Loss for Person Re-identification](https://arxiv.org/abs/1703.07737) | ||
|
||
[Andrew Ng's Triplet Loss Lecture](https://www.coursera.org/learn/convolutional-neural-networks/lecture/HuUtN/triplet-loss) | ||
|
||
[Triplet Loss and Online Triplet Mining in TensorFlow](https://omoindrot.github.io/triplet-loss) | ||
|
||
--- | ||
|
||
### :postal_horn: Keras | ||
|
||
Implementations: Triplet Loss with Batch Hard sampling and random sampling and Contrastive Loss | ||
|
||
--- | ||
|
||
|
||
### :zap: Fastai | ||
|
||
Code Credits: [Alex Fitts](http://afitts.github.io/2018/12/03/humpback-siamese/) and [Radek](https://github.com/radekosmulski/whale/blob/master/siamese_network_prototype.ipynb) | ||
|
||
Implementations: Triplet Loss with random sampling and Contrastive Loss | ||
|
||
--- |