A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation

• URL: http://arxiv.org/abs/2010.10368v2
• Date: Mon, 26 Oct 2020 19:29:06 GMT
• Title: A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation
• Authors: Ali Akbari, Muhammad Awais, Zhen-Hua Feng, Ammarah Farooq and Josef Kittler
• Abstract summary: We advocate a cross-dataset protocol for age estimation benchmarking. We propose a novel loss function that is more effective for neural network training.
• Score: 37.107335288543624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The most existing studies in the facial age estimation assume training and test images are captured under similar shooting conditions. However, this is rarely valid in real-world applications, where training and test sets usually have different characteristics. In this paper, we advocate a cross-dataset protocol for age estimation benchmarking. In order to improve the cross-dataset age estimation performance, we mitigate the inherent bias caused by the learning algorithm itself. To this end, we propose a novel loss function that is more effective for neural network training. The relative smoothness of the proposed loss function is its advantage with regards to the optimisation process performed by stochastic gradient descent (SGD). Compared with existing loss functions, the lower gradient of the proposed loss function leads to the convergence of SGD to a better optimum point, and consequently a better generalisation. The cross-dataset experimental results demonstrate the superiority of the proposed method over the state-of-the-art algorithms in terms of accuracy and generalisation capability.

Related papers

• Gradient Descent Efficiency Index [0.0]
  This study introduces a new efficiency metric, Ek, designed to quantify the effectiveness of each iteration. The proposed metric accounts for both the relative change in error and the stability of the loss function across iterations. Ek has the potential to guide more informed decisions in the selection and tuning of optimization algorithms in machine learning applications.
  arXiv  Detail & Related papers  (2024-10-25T10:22:22Z)
• Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models [0.0]
  We introduce a distance called Reduced Jeffries-Matusita as a loss function for training deep classification models to reduce the over-fitting issue. The results show that the new distance measure stabilizes the training process significantly, enhances the generalization ability, and improves the performance of the models in the Accuracy and F1-score metrics.
  arXiv  Detail & Related papers  (2024-03-13T10:51:38Z)
• Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks [22.348887008547653]
  This work introduces Random Linear Projections (RLP) loss, a novel approach that enhances training efficiency by leveraging geometric relationships within the data. Our empirical evaluations, conducted across benchmark datasets and synthetic examples, demonstrate that neural networks trained with RLP loss outperform those trained with traditional loss functions.
  arXiv  Detail & Related papers  (2023-11-21T05:22:39Z)
• Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification [2.922007656878633]
  We propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks.
  arXiv  Detail & Related papers  (2022-09-18T10:29:02Z)
• Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice [52.11183787786718]
  Fine-tuning a pre-trained model on the target data is widely used in many deep learning applications. Recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy. We propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task.
  arXiv  Detail & Related papers  (2021-11-24T06:18:32Z)
• Mixing between the Cross Entropy and the Expectation Loss Terms [89.30385901335323]
  Cross entropy loss tends to focus on hard to classify samples during training. We show that adding to the optimization goal the expectation loss helps the network to achieve better accuracy. Our experiments show that the new training protocol improves performance across a diverse set of classification domains.
  arXiv  Detail & Related papers  (2021-09-12T23:14:06Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Adversarially Robust Learning via Entropic Regularization [31.6158163883893]
  We propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. Our approach achieves competitive (or better) performance in terms of robust classification accuracy.
  arXiv  Detail & Related papers  (2020-08-27T18:54:43Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)
• The Break-Even Point on Optimization Trajectories of Deep Neural Networks [64.7563588124004]
  We argue for the existence of the "break-even" point on this trajectory. We show that using a large learning rate in the initial phase of training reduces the variance of the gradient. We also show that using a low learning rate results in bad conditioning of the loss surface even for a neural network with batch normalization layers.
  arXiv  Detail & Related papers  (2020-02-21T22:55:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.