A deep dive into 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.
Claim actually close to 1% improvement on image net data set¹.
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.
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.
The above is pretty self explanatory.
Loss, the main flavor of this paper is understanding the self supervised contrastive loss and supervised contrastive loss.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.