Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps

• URL: http://arxiv.org/abs/2208.10033v1
• Date: Mon, 22 Aug 2022 03:30:18 GMT
• Title: Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps
• Authors: Jay Bishnu and Andrew Gondoputro
• Abstract summary: Training on large datasets often requires excessive system resources and an infeasible amount of time. For supervised learning, large datasets require more time for manually labeling samples. We propose a method of curating smaller datasets with comparable out-of-distribution model accuracy after an initial training session.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Rapid development in deep learning model construction has prompted an increased need for appropriate training data. The popularity of large datasets - sometimes known as "big data" - has diverted attention from assessing their quality. Training on large datasets often requires excessive system resources and an infeasible amount of time. Furthermore, the supervised machine learning process has yet to be fully automated: for supervised learning, large datasets require more time for manually labeling samples. We propose a method of curating smaller datasets with comparable out-of-distribution model accuracy after an initial training session using an appropriate distribution of samples classified by how difficult it is for a model to learn from them.

Related papers

• Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review [50.78587571704713]
  Large Language Model (LLM) pretraining traditionally relies on autoregressive language modeling on randomly sampled data blocks from web-scale datasets. We take inspiration from human learning techniques like spaced repetition to hypothesize that random data sampling for LLMs leads to high training cost and low quality models which tend to forget data. In order to effectively commit web-scale information to long-term memory, we propose the LFR (Learn, Focus, and Review) pedagogy.
  arXiv  Detail & Related papers  (2024-09-10T00:59:18Z)
• Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach [36.47860223750303]
  We consider the problem of automatic curation of high-quality datasets for self-supervised pre-training. We propose a clustering-based approach for building ones satisfying all these criteria. Our method involves successive and hierarchical applications of $k$-means on a large and diverse data repository.
  arXiv  Detail & Related papers  (2024-05-24T14:58:51Z)
• Teacher Guided Training: An Efficient Framework for Knowledge Transfer [86.6784627427194]
  We propose the teacher-guided training (TGT) framework for training a high-quality compact model. TGT exploits the fact that the teacher has acquired a good representation of the underlying data domain. We find that TGT can improve accuracy on several image classification benchmarks and a range of text classification and retrieval tasks.
  arXiv  Detail & Related papers  (2022-08-14T10:33:58Z)
• CHALLENGER: Training with Attribution Maps [63.736435657236505]
  We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
  arXiv  Detail & Related papers  (2022-05-30T13:34:46Z)
• Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey [1.5469452301122175]
  Deep learning models rely on the abundance of labelled observations to train a prospective model. It is expensive to gather labelled observations of data, making the usage of deep learning models not ideal. In many situations different unlabelled data sources might be available. This raises the risk of a significant distribution mismatch between the labelled and unlabelled datasets.
  arXiv  Detail & Related papers  (2022-03-01T02:46:00Z)
• Fix your Models by Fixing your Datasets [0.6058427379240697]
  Current machine learning tools lack streamlined processes for improving the data quality. We introduce a systematic framework for finding noisy or mislabelled samples in the dataset. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies.
  arXiv  Detail & Related papers  (2021-12-15T02:41:50Z)
• Understanding the World Through Action [91.3755431537592]
  I will argue that a general, principled, and powerful framework for utilizing unlabeled data can be derived from reinforcement learning. I will discuss how such a procedure is more closely aligned with potential downstream tasks.
  arXiv  Detail & Related papers  (2021-10-24T22:33:52Z)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• Online feature selection for rapid, low-overhead learning in networked systems [0.0]
  We present an online algorithm, called OSFS, that selects a small feature set from a large number of available data sources. We find that OSFS requires several hundreds measurements to reduce the number of data sources by two orders of magnitude.
  arXiv  Detail & Related papers  (2020-10-28T12:00:42Z)
• Learning from Imperfect Annotations [15.306536555936692]
  Many machine learning systems today are trained on large amounts of human-annotated data. We propose a new end-to-end framework that enables us to merge the aggregation step with model training. We show accuracy gains of up to 25% over the current state-of-the-art approaches for aggregating annotations.
  arXiv  Detail & Related papers  (2020-04-07T15:21:08Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

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.