Cover Image

Interpretable Few-shot Learning with Online Attribute Selection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zarei, Mohammad Reza, Komeili, Majid
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis
by: Davoodi, Omid, et al.
Published: (2023)

Efficient Few-shot Identity Preserving Attribute Editing for 3D-aware Deep Generative Models
by: Vinod, Vishal
Published: (2025)

Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning
by: Jayakody, Dilith, et al.
Published: (2023)

Visual Authority and the Rhetoric of Health Misinformation: A Multimodal Analysis of Social Media Videos
by: Zarei, Mohammad Reza, et al.
Published: (2025)

Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
by: Ilic, Filip, et al.
Published: (2024)

Feedforward Few-shot Species Range Estimation
by: Lange, Christian, et al.
Published: (2025)

Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
by: Hu, Minyang, et al.
Published: (2024)

VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation
by: Chen, Yu-hsuan, et al.
Published: (2024)

Flatness Improves Backbone Generalisation in Few-shot Classification
by: Li, Rui, et al.
Published: (2024)

DataDream: Few-shot Guided Dataset Generation
by: Kim, Jae Myung, et al.
Published: (2024)

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
by: Chen, Ruoyu, et al.
Published: (2025)

Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
by: Ma, Yintai, et al.
Published: (2024)

A Deep Positive-Negative Prototype Approach to Integrated Prototypical Discriminative Learning
by: Zarei-Sabzevar, Ramin, et al.
Published: (2025)

L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout
by: Song, Heda, et al.
Published: (2019)

Overcoming Support Dilution for Robust Few-shot Semantic Segmentation
by: Tang, Wailing, et al.
Published: (2025)

Improving Diversity in Black-box Few-shot Knowledge Distillation
by: Vo, Tri-Nhan, et al.
Published: (2026)

Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis
by: Kudo, Shoma, et al.
Published: (2024)

Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP
by: Abbasi, Reza, et al.
Published: (2024)

VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning
by: Lin, Han, et al.
Published: (2024)

AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
by: Tang, Yuwei, et al.
Published: (2024)

A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks
by: Hobson, David G, et al.
Published: (2024)

Verbalized Representation Learning for Interpretable Few-Shot Generalization
by: Yang, Cheng-Fu, et al.
Published: (2024)

Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration
by: Hebbalaguppe, Ramya, et al.
Published: (2025)

Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
by: Asadi, Nader, et al.
Published: (2024)

Erasing Concepts from Text-to-Image Diffusion Models with Few-shot Unlearning
by: Fuchi, Masane, et al.
Published: (2024)

Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
by: Luo, Xu, et al.
Published: (2026)

AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
by: Saha, Surojit, et al.
Published: (2025)

DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification
by: Reuss, Joana, et al.
Published: (2026)

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
by: Chen, Shengzhuang, et al.
Published: (2024)

Few-shot multi-token DreamBooth with LoRa for style-consistent character generation
by: Pascual, Ruben, et al.
Published: (2025)

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
by: Tian, Hongduan, et al.
Published: (2024)

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains
by: Xu, Xin, et al.
Published: (2025)

12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
by: Wibowo, Yoga Esa, et al.
Published: (2024)

Deep Positive-Negative Prototypes for Adversarially Robust Discriminative Prototypical Learning
by: Sabzevar, Ramin Zarei, et al.
Published: (2025)

Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
by: Xue, Yihao, et al.
Published: (2023)

TactileEval: A Step Towards Automated Fine-Grained Evaluation and Editing of Tactile Graphics
by: Khan, Adnan, et al.
Published: (2026)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
by: Bhalla, Usha, et al.
Published: (2023)

Towards Interpretable and Efficient Attention: Compressing All by Contracting a Few
by: Wen, Qishuai, et al.
Published: (2025)

HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning
by: Li, Jianfeng, et al.
Published: (2024)

Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments
by: Atha, Deegan, et al.
Published: (2024)