Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

Overview

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

The implementation and scripts for training and evaluation can be found in corresponding directory for each task.

Example implementation

import torch
import torch.nn as nn
from torch.nn import functional as F

class EncourageLoss(nn.Module):
    def __init__(self, log_end=1.0, reduction='mean'):
        super(EncourageLoss, self).__init__()
        self.log_end = log_end
        self.reduction = reduction

    def forward(self, input, target):
        lprobs = F.log_softmax(input)  # logp
        probs = torch.exp(lprobs)
        bonus = torch.log(torch.clamp((torch.ones_like(probs) - probs), min=1e-5))  # log(1-p)
        if self.log_end != 1.0:  # end of the log curve in conservative bonus # e.g. 0.5  work for all settings
            log_end = self.log_end
            y_log_end = torch.log(torch.ones_like(probs) - log_end)
            bonus_after_log_end = 1/(log_end - torch.ones_like(probs)) * (probs-log_end) + y_log_end
            bonus = torch.where(probs > log_end, bonus_after_log_end, bonus)
        loss = F.nll_loss(lprobs-bonus, target.view(-1), reduction=self.reduction)
        return loss
Owner
LancoPKU
Language Computing and Machine Learning Group (Xu Sun's group) at Peking University
LancoPKU
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