George  Koelpin

George Koelpin

1603076400

A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Claim actually close to 1% improvement on image net data set¹.

Image for post

Classification accuracy from the paper¹

Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well.

Image for post

Architecture and training process from the paper¹

def forward(self, x): 
feat = self.encoder(x) #normalizing the 128 vector is required Code self.encoder = resnet50() self.head = nn.Linear(2048, 128)  
feat = F.normalize(self.head(feat), dim=1) 
return feat

As shown in the figure training is done in two-stage.

  • Train using contrastive loss (two variations)
  • freeze the learned representations and then learn a classifier on a linear layer using a softmax loss. (From the paper)

The above is pretty self explanatory.

Loss, the main flavor of this paper is understanding the self supervised contrastive loss and supervised contrastive loss.

#programming #data-science

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A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Benefits of Taking Education Loan to Study Abroad : edu-visa

As the cost of education is getting higher rapidly, a lot of students have to give up on their dreams to study abroad. Canada is known as the best country to study abroad for Indian students. You will need approximately between INR 12,50,000 to 19,00,000 a year if you’re an Indian citizen and looking forward to studying in Canada…Read more

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George  Koelpin

George Koelpin

1603076400

A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Claim actually close to 1% improvement on image net data set¹.

Image for post

Classification accuracy from the paper¹

Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well.

Image for post

Architecture and training process from the paper¹

def forward(self, x): 
feat = self.encoder(x) #normalizing the 128 vector is required Code self.encoder = resnet50() self.head = nn.Linear(2048, 128)  
feat = F.normalize(self.head(feat), dim=1) 
return feat

As shown in the figure training is done in two-stage.

  • Train using contrastive loss (two variations)
  • freeze the learned representations and then learn a classifier on a linear layer using a softmax loss. (From the paper)

The above is pretty self explanatory.

Loss, the main flavor of this paper is understanding the self supervised contrastive loss and supervised contrastive loss.

#programming #data-science

George  Koelpin

George Koelpin

1601064000

A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Supervised Contrastive Learning paper claims a big deal about supervised learning and cross entropy loss vs supervised contrastive loss for better image representation and classification tasks. Lets go in depth in this paper what is about.

Claim actually close to 1% improvement on image net data set¹.

Image for post

#data-science #machine-learning #ai #neural-networks #deep-learning

Sofia  Maggio

Sofia Maggio

1620847740

10 Self-Supervised Learning Frameworks & Libraries To Use In 2021

Self-supervised learning is gathering steam, slowly but surely. A relatively new technique, self-supervised learning is nothing but training unlabeled data without human supervision. Yann LeCun described it best: Reinforcement learning is like a cherry on a cake, supervised learning is the icing on the cake, and self-supervised learning is the cake. In self-supervised or unsupervised learning, the system learns to predict part of its input from already existing inputs, he said.

Most tech evangelists liken self-supervised learning models to young children, always curious and learning new information from observation. The latest examples of self-supervision include Facebook’s DINO and ViSSL (Vision library for Self-Supervised Learning); Google’s SimCLROpenSelfSup and SfMLearner, etc.

Below, we have curated a list of the most popular self-supervised learning models, frameworks, and libraries.

DINO

DINO, a self-supervised learning vision transformers (ViT), is used to segment unlabelled and random images and videos without supervision. In other words, self DIstillation with NO labels. The model generates high accurate segmentation with self-supervised learning and suitable architecture. Also, DINO requires limited computing resources to train models.

Lightly

Lightly is a computer vision framework for self-supervised learning. It helps in understanding and filtering raw image data and can be applied before any data annotation step. The learned representations can further analyse and visualise datasets, alongside selecting a core set of samples.

s3prl

s3prl is an open-source toolkit that stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit and are used in multiple downstream tasks.

#developers corner #machine learning libraries #self-supervision learning tools #self-supervision libraries

Study in France Student Visa, Universities,Tuition Cost, Admission Process

There are many places to study worldwide. If you are looking for a country with great education along with cultural experience, then France is the place for you. Every year many students go to study in France. You can pursue various programs, bachelor’s o and even your masters in France. There are many recognized universities to study in France for international students. Here at Abroad Admission we provide quality education for foreign studies. So if you are planning to study abroad in france, then we are there to assist you professionally!

For more query please visit:-
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