Evolving parametrized Loss for Image Classification Learning on Small Datasets

• URL: http://arxiv.org/abs/2103.08249v2
• Date: Mon, 30 Oct 2023 07:27:01 GMT
• Title: Evolving parametrized Loss for Image Classification Learning on Small Datasets
• Authors: Zhaoyang Hai, Xiabi Liu
• Abstract summary: This paper proposes a meta-learning approach to evolving a parametrized loss function, which is called Meta-Loss Network (MLN) In our approach, the MLN is embedded in the framework of classification learning as a differentiable objective function. Experiment results demonstrate that the MLN effectively improved generalization compared to classical cross-entropy error and mean squared error.
• Score: 1.4685355149711303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a meta-learning approach to evolving a parametrized loss function, which is called Meta-Loss Network (MLN), for training the image classification learning on small datasets. In our approach, the MLN is embedded in the framework of classification learning as a differentiable objective function. The MLN is evolved with the Evolutionary Strategy algorithm (ES) to an optimized loss function, such that a classifier, which optimized to minimize this loss, will achieve a good generalization effect. A classifier learns on a small training dataset to minimize MLN with Stochastic Gradient Descent (SGD), and then the MLN is evolved with the precision of the small-dataset-updated classifier on a large validation dataset. In order to evaluate our approach, the MLN is trained with a large number of small sample learning tasks sampled from FashionMNIST and tested on validation tasks sampled from FashionMNIST and CIFAR10. Experiment results demonstrate that the MLN effectively improved generalization compared to classical cross-entropy error and mean squared error.

Related papers

• Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks [5.285761906707429]
  We propose a meta-learning algorithm, named Meta-GCN, for adaptively learning the example weights. We have shown that Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms of accuracy.
  arXiv  Detail & Related papers  (2024-06-24T18:59:24Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning [70.52689048213398]
  This paper studies the performance of overfitted meta-learning under a linear regression model with Gaussian features. We find new and interesting properties that do not exist in single-task linear regression. Our analysis suggests that benign overfitting is more significant and easier to observe when the noise and the diversity/fluctuation of the ground truth of each training task are large.
  arXiv  Detail & Related papers  (2023-04-09T20:36:13Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• An Adaptive Plug-and-Play Network for Few-Shot Learning [12.023266104119289]
  Few-shot learning requires a model to classify new samples after learning from only a few samples. Deep networks and complex metrics tend to induce overfitting, making it difficult to further improve the performance. We propose plug-and-play model-adaptive resizer (MAR) and adaptive similarity metric (ASM) without any other losses.
  arXiv  Detail & Related papers  (2023-02-18T13:25:04Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• Simple and Robust Loss Design for Multi-Label Learning with Missing Labels [14.7306301893944]
  We propose two simple yet effective methods via robust loss design based on an observation a model can identify missing labels during training. The first is a novel robust loss for negatives, namely the Hill loss, which re-weights negatives in the shape of a hill to alleviate the effect of false negatives. The second is a self-paced loss correction (SPLC) method, which uses a loss derived from the maximum likelihood criterion under an approximate distribution of missing labels.
  arXiv  Detail & Related papers  (2021-12-13T11:39:19Z)
• Generating meta-learning tasks to evolve parametric loss for classification learning [1.1355370218310157]
  In existing meta-learning approaches, learning tasks for training meta-models are usually collected from public datasets. We propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data.
  arXiv  Detail & Related papers  (2021-11-20T13:07:55Z)
• B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning [2.9189409618561966]
  We propose a Bayesian neural network based MAML algorithm, which we refer to as the B-SMALL algorithm. We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model.
  arXiv  Detail & Related papers  (2021-01-01T09:19:48Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)
• MMCGAN: Generative Adversarial Network with Explicit Manifold Prior [78.58159882218378]
  We propose to employ explicit manifold learning as prior to alleviate mode collapse and stabilize training of GAN. Our experiments on both the toy data and real datasets show the effectiveness of MMCGAN in alleviating mode collapse, stabilizing training, and improving the quality of generated samples.
  arXiv  Detail & Related papers  (2020-06-18T07:38:54Z)

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.