An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Overview

Decoupled-Contrastive-Learning

This repository is an implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Requirements

  • Pytorch
  • Numpy

Usage Example

import torch
import torchvision.models as models

from loss import dcl

resnet18 = models.resnet18()
random_input = torch.rand((10, 3, 244, 244))
output = resnet18(random_input)

# for DCL
loss_fn = dcl.DCL(temperature=0.5)
loss = loss_fn(output, output)  # loss = tensor(-0.2726, grad_fn=
   

# for DCLW
loss_fn = dcl.DCLW(temperature=0.5, sigma=0.5)
loss = loss_fn(output, output)  # loss = tensor(38.8402, grad_fn=
   
    )
   

Results

Will be added shortly.

Owner
Ramin Nakhli
[email protected]: Self-Supervised models / GNNs - Grad CS
Ramin Nakhli
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