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

Last update: Dec 04, 2022

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

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