Bigeminal Priors Variational auto-encoder

• URL: http://arxiv.org/abs/2010.01819v1
• Date: Mon, 5 Oct 2020 07:10:52 GMT
• Title: Bigeminal Priors Variational auto-encoder
• Authors: Xuming Ran, Mingkun Xu, Qi Xu, Huihui Zhou, Quanying Liu
• Abstract summary: Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised learning. We introduce a new model, namely Bigeminal Priors Variational auto-encoder (BPVAE), to address this phenomenon. BPVAE learns two datasets' features, assigning a higher likelihood for the training dataset than the simple dataset.
• Score: 5.430048915427229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised learning. The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset. However, recent works reported a phenomenon that VAE recognizes some OOD samples as ID by assigning a higher likelihood to the OOD inputs compared to the one from ID. In this work, we introduce a new model, namely Bigeminal Priors Variational auto-encoder (BPVAE), to address this phenomenon. The BPVAE aims to enhance the robustness of the VAEs by combing the power of VAE with the two independent priors that belong to the training dataset and simple dataset, which complexity is lower than the training dataset, respectively. BPVAE learns two datasets'features, assigning a higher likelihood for the training dataset than the simple dataset. In this way, we can use BPVAE's density estimate for detecting the OOD samples. Quantitative experimental results suggest that our model has better generalization capability and stronger robustness than the standard VAEs, proving the effectiveness of the proposed approach of hybrid learning by collaborative priors. Overall, this work paves a new avenue to potentially overcome the OOD problem via multiple latent priors modeling.

Related papers

• Can OOD Object Detectors Learn from Foundation Models? [56.03404530594071]
  Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models.
  arXiv  Detail & Related papers  (2024-09-08T17:28:22Z)
• Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders [56.47577824219207]
  In this paper, we unveil the hidden costs associated with intrusive fine-tuning techniques. We introduce a new model reprogramming approach for fine-tuning, which we name Reprogrammer. Our empirical evidence reveals that Reprogrammer is less intrusive and yields superior downstream models.
  arXiv  Detail & Related papers  (2024-03-16T04:19:48Z)
• Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources [73.28967478098107]
  Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data. It is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerning OOD patterns. We propose a data generation-based learning method named Auxiliary Task-based OOD Learning (ATOL) that can relieve the mistaken OOD generation.
  arXiv  Detail & Related papers  (2023-11-06T16:26:52Z)
• Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder [3.498694457257263]
  We propose a novel VAE-based score called Error Reduction (ER) for OOD detection. ER is based on a VAE that takes a lossy version of the training set as inputs and the original set as targets.
  arXiv  Detail & Related papers  (2023-09-05T09:42:15Z)
• Pseudo-OOD training for robust language models [78.15712542481859]
  OOD detection is a key component of a reliable machine-learning model for any industry-scale application. We propose POORE - POsthoc pseudo-Ood REgularization, that generates pseudo-OOD samples using in-distribution (IND) data. We extensively evaluate our framework on three real-world dialogue systems, achieving new state-of-the-art in OOD detection.
  arXiv  Detail & Related papers  (2022-10-17T14:32:02Z)
• Are Sample-Efficient NLP Models More Robust? [90.54786862811183]
  We investigate the relationship between sample efficiency (amount of data needed to reach a given ID accuracy) and robustness (how models fare on OOD evaluation) We find that higher sample efficiency is only correlated with better average OOD robustness on some modeling interventions and tasks, but not others. These results suggest that general-purpose methods for improving sample efficiency are unlikely to yield universal OOD robustness improvements, since such improvements are highly dataset- and task-dependent.
  arXiv  Detail & Related papers  (2022-10-12T17:54:59Z)
• Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning [54.61762276179205]
  We propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples. We validate our contributions by achieving competitive performance on the OOD dataset VQA-CP v2 while preserving robust performance on the ID dataset VQA v2.
  arXiv  Detail & Related papers  (2022-10-10T11:05:21Z)
• Training OOD Detectors in their Natural Habitats [31.565635192716712]
  Out-of-distribution (OOD) detection is important for machine learning models deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for improved OOD detection. We propose a novel framework that leverages wild mixture data -- that naturally consists of both ID and OOD samples.
  arXiv  Detail & Related papers  (2022-02-07T15:38:39Z)
• Energy-bounded Learning for Robust Models of Code [16.592638312365164]
  In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on. We propose the use of an energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models.
  arXiv  Detail & Related papers  (2021-12-20T06:28:56Z)
• EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference [4.826988182025783]
  Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs to a server. While this setup works cost-effectively for successful inferences, it severely underperforms when the model faces input samples on which the model was not trained. We propose a novel lightweight OOD detection approach that mines important features from the shallow layers of a pretrained CNN model.
  arXiv  Detail & Related papers  (2021-06-25T18:43:23Z)
• Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation [5.430048915427229]
  Variational autoencoders (VAEs) are influential generative models with rich representation capabilities. VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE.
  arXiv  Detail & Related papers  (2020-07-16T06:02:18Z)

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.