Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts

• URL: http://arxiv.org/abs/2308.07538v1
• Date: Tue, 15 Aug 2023 02:40:32 GMT
• Title: Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts
• Authors: Guillermo Cabrera-Vives, C\'esar Bolivar, Francisco F\"orster, Alejandra M. Mu\~noz Arancibia, Manuel P\'erez-Carrasco, Esteban Reyes
• Abstract summary: We study Domain Adaptation (DA) for real/bogus classification of astronomical alerts using four different datasets: HiTS, DES, ATLAS, and ZTF. We study the domain shift between these datasets, and improve a naive deep learning classification model by using a fine tuning approach and semi-supervised deep DA via Minimax Entropy (MME) We find that both the fine tuning and MME models improve significantly the base model with as few as one labeled item per class coming from the target dataset, but that the MME does not compromise its performance on the source dataset.
• Score: 39.58317527488534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work, we study Domain Adaptation (DA) for real/bogus classification of astronomical alerts using four different datasets: HiTS, DES, ATLAS, and ZTF. We study the domain shift between these datasets, and improve a naive deep learning classification model by using a fine tuning approach and semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced accuracy of these models for different source-target scenarios. We find that both the fine tuning and MME models improve significantly the base model with as few as one labeled item per class coming from the target dataset, but that the MME does not compromise its performance on the source dataset.

Related papers

• Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models [49.439311430360284]
  We introduce a novel data synthesis method inspired by contrastive learning and image difference captioning. Our key idea involves challenging the model to discern both matching and distinct elements. We leverage this generated dataset to fine-tune state-of-the-art (SOTA) MLLMs.
  arXiv  Detail & Related papers  (2024-08-08T17:10:16Z)
• Transfer Learning with Point Transformers [3.678615604632945]
  Point Transformers are state-of-the-art models for classification, segmentation, and detection on Point Cloud data. We explore two things: classification performance of these attention based networks on ModelNet10 dataset and then, we use the trained model to classify 3D MNIST dataset after finetuning.
  arXiv  Detail & Related papers  (2024-04-01T01:23:58Z)
• FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis [0.7751705157998379]
  The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages multiple fine-tuned models aiming to improve performance on In-Domain (ID) tasks and enhance robustness against Out-of-Distribution (OOD) datasets. We propose a hierarchical merging approach that involves local and global aggregation of models at various levels.
  arXiv  Detail & Related papers  (2024-03-20T06:48:48Z)
• Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets [40.19690479537335]
  We show that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information.
  arXiv  Detail & Related papers  (2023-11-02T20:40:21Z)
• SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation [62.889835139583965]
  We introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
  arXiv  Detail & Related papers  (2023-04-06T17:36:23Z)
• Impact of Dataset on Acoustic Models for Automatic Speech Recognition [0.0]
  In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic modelling. The GMM models are widely used to create the alignments of the training data for the hybrid deep neural network model. This work aims to investigate the impact of dataset size variations on the performance of various GMM-HMM Acoustic Models.
  arXiv  Detail & Related papers  (2022-03-25T11:41:49Z)
• Contrastive Neighborhood Alignment [81.65103777329874]
  We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features. The target model aims to mimic the local structure of the source representation space using a contrastive loss. CNA is illustrated in three scenarios: manifold learning, where the model maintains the local topology of the original data in a dimension-reduced space; model distillation, where a small student model is trained to mimic a larger teacher; and legacy model update, where an older model is replaced by a more powerful one.
  arXiv  Detail & Related papers  (2022-01-06T04:58:31Z)
• Manifold Topology Divergence: a Framework for Comparing Data Manifolds [109.0784952256104]
  We develop a framework for comparing data manifold, aimed at the evaluation of deep generative models. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance.
  arXiv  Detail & Related papers  (2021-06-08T00:30:43Z)
• Graphical Modeling for Multi-Source Domain Adaptation [56.05348879528149]
  Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain. We propose two types of graphical models,i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random Field for MSDA (MRF-MSDA) We evaluate these two models on four standard benchmark data sets of MSDA with distinct domain shift and data complexity.
  arXiv  Detail & Related papers  (2021-04-27T09:04:22Z)
• DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains [0.0]
  In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey.
  arXiv  Detail & Related papers  (2021-03-02T00:24:10Z)

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.