Stylist: Style-Driven Feature Ranking for Robust Novelty Detection
- URL: http://arxiv.org/abs/2310.03738v1
- Date: Thu, 5 Oct 2023 17:58:32 GMT
- Title: Stylist: Style-Driven Feature Ranking for Robust Novelty Detection
- Authors: Stefan Smeu, Elena Burceanu, Emanuela Haller, Andrei Liviu Nicolicioiu
- Abstract summary: We propose to use the formalization of separating into semantic or content changes, that are relevant to our task, and style changes, that are irrelevant.
Within this formalization, we define the robust novelty detection as the task of finding semantic changes while being robust to style distributional shifts.
We show that our selection manages to remove features responsible for spurious correlations and improve novelty detection performance.
- Score: 8.402607231390606
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Novelty detection aims at finding samples that differ in some form from the
distribution of seen samples. But not all changes are created equal. Data can
suffer a multitude of distribution shifts, and we might want to detect only
some types of relevant changes. Similar to works in out-of-distribution
generalization, we propose to use the formalization of separating into semantic
or content changes, that are relevant to our task, and style changes, that are
irrelevant. Within this formalization, we define the robust novelty detection
as the task of finding semantic changes while being robust to style
distributional shifts. Leveraging pretrained, large-scale model
representations, we introduce Stylist, a novel method that focuses on dropping
environment-biased features. First, we compute a per-feature score based on the
feature distribution distances between environments. Next, we show that our
selection manages to remove features responsible for spurious correlations and
improve novelty detection performance. For evaluation, we adapt domain
generalization datasets to our task and analyze the methods behaviors. We
additionally built a large synthetic dataset where we have control over the
spurious correlations degree. We prove that our selection mechanism improves
novelty detection algorithms across multiple datasets, containing both
stylistic and content shifts.
Related papers
- Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning [51.177789437682954]
Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones.
Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown.
We propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration.
arXiv Detail & Related papers (2025-02-11T13:57:30Z) - Label-template based Few-Shot Text Classification with Contrastive Learning [7.964862748983985]
We propose a simple and effective few-shot text classification framework.
Label templates are embedded into input sentences to fully utilize the potential value of class labels.
supervised contrastive learning is utilized to model the interaction information between support samples and query samples.
arXiv Detail & Related papers (2024-12-13T12:51:50Z) - Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance.
DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator.
Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
arXiv Detail & Related papers (2024-07-20T01:34:13Z) - Selective Domain-Invariant Feature for Generalizable Deepfake Detection [21.671221284842847]
We propose a novel framework which reduces the sensitivity to face forgery by fusing content features and styles.
Both qualitative and quantitative results in existing benchmarks and proposals demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2024-03-19T13:09:19Z) - Common-Sense Bias Modeling for Classification Tasks [15.683471433842492]
We propose a novel framework to extract comprehensive biases in image datasets based on textual descriptions.
Our method uncovers novel model biases in multiple image benchmark datasets.
The discovered bias can be mitigated by simple data re-weighting to de-correlate the features.
arXiv Detail & Related papers (2024-01-24T03:56:07Z) - Environment-biased Feature Ranking for Novelty Detection Robustness [8.402607231390606]
We tackle the problem of robust novelty detection, where we aim to detect novelties in terms of semantic content.
We propose a method that starts with a pretrained embedding and a multi-env setup and manages to rank the features based on their environment-focus.
arXiv Detail & Related papers (2023-09-21T17:58:26Z) - Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot
Text Classification Tasks [75.42002070547267]
We propose a self evolution learning (SE) based mixup approach for data augmentation in text classification.
We introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up.
arXiv Detail & Related papers (2023-05-22T23:43:23Z) - ContraFeat: Contrasting Deep Features for Semantic Discovery [102.4163768995288]
StyleGAN has shown strong potential for disentangled semantic control.
Existing semantic discovery methods on StyleGAN rely on manual selection of modified latent layers to obtain satisfactory manipulation results.
We propose a model that automates this process and achieves state-of-the-art semantic discovery performance.
arXiv Detail & Related papers (2022-12-14T15:22:13Z) - Intra-class Adaptive Augmentation with Neighbor Correction for Deep
Metric Learning [99.14132861655223]
We propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning.
We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining.
Our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%.
arXiv Detail & Related papers (2022-11-29T14:52:38Z) - Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels [26.542718087103665]
SemiMatch is a semi-supervised solution for establishing dense correspondences across semantically similar images.
Our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target.
In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks, especially on PF-Willow by a large margin.
arXiv Detail & Related papers (2022-03-30T03:52:50Z) - Dynamic Semantic Matching and Aggregation Network for Few-shot Intent
Detection [69.2370349274216]
Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances.
Semantic components are distilled from utterances via multi-head self-attention.
Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
arXiv Detail & Related papers (2020-10-06T05:16:38Z)
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.