GeNIe: Generative Hard Negative Images Through Diffusion

Conceitos essenciais

Generative models like GeNIe leverage diffusion techniques to create challenging samples for deep learning models through data augmentation.

Resumo

Directory:

1. Abstract
2. Introduction
3. Proposed Method: GeNIe
4. Algorithm 1: GeNIe-Ada
5. Experiments
   • Few-shot Classification
   • Long-Tailed Classification
   • Fine-grained Few-shot Classification
6. Ablation and Analysis
7. Related Work

Abstract:

• Data augmentation is crucial for training deep models to prevent overfitting.
• GeNIe introduces a novel augmentation method using diffusion models.
• GeNIe generates hard negative samples by blending source images with text prompts.

Introduction:

• Augmentation enhances model robustness and adaptability.
• Traditional methods like MixUp and CutMix may not produce natural images.
• Generative models like GeNIe use diffusion techniques for data augmentation.

Proposed Method: GeNIe:

• GeNIe leverages latent diffusion models conditioned on text prompts.
• It generates hard negative samples by blending source images with target categories.

Algorithm 1: GeNIe-Ada:

• Adaptive noise level selection strategy improves performance in various scenarios.

Experiments:

Few-shot Classification:

• Evaluates the impact of GeNIe on few-shot classification settings.

Long-Tailed Classification:

• Assesses the effectiveness of GeNIe in long-tailed data settings.

Fine-grained Few-shot Classification:

• Compares the performance of GeNIe with other text-based augmentation techniques.

Ablation and Analysis:

• Analyzes the semantic shift from source to target class using different noise levels.
• Discusses the label consistency of generated samples and the effect of noise on model performance.

Related Work:

• Discusses data augmentation, generative models, language-guided recognition models, and few-shot learning in relation to GeNIe's approach.

Estatísticas

データ拡張は深層モデルの過学習を防ぐために重要です。
最近の進歩により、拡散モデルなどの生成AIがより洗練された拡張技術を可能にしました。

Citações

Principais Insights Extraídos De

GeNIe

by Soroush Abba... às arxiv.org 03-26-2024

https://arxiv.org/pdf/2312.02548.pdf

Perguntas Mais Profundas

データ拡張方法としてのGeNIeの有効性を他の分野で評価することは可能ですか？

GeNIeは、生成的AIを活用したデータ拡張手法であり、画像生成において自然な画像に類似したデータを生成します。このアプローチは、他の分野でも有効性が期待されます。例えば、医療画像解析では限られたトレーニングデータからモデルを訓練する際に適用できる可能性があります。また、製造業や自動車産業などでも部品や製品の検査や品質管理において利用されるかもしれません。さらに、地理情報システム（GIS）や天文学などの領域でも異常検出やパターン認識などへの応用が考えられます。