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Generalized Zero-Shot Learning

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평가표: 12
보존된 평가 기록: 26

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sion1/dfan

공식 코드

Dual Feature Augmentation Network for Generalized Zero-shot Learning

https://github.com/skelemoa/synse-zsl

공식 코드

Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition

https://github.com/stevehuanghe/GDAN

공식 코드

Generative Dual Adversarial Network for Generalized Zero-shot Learning

https://github.com/sumitramalagi/unseen-classes-at-a-later-time

공식 코드

Unseen Classes at a Later Time? No Problem

https://github.com/twuilliam/bias-gzsl

공식 코드

Bias-Awareness for Zero-Shot Learning the Seen and Unseen

https://github.com/universome/czsl

공식 코드

Class Normalization for (Continual)? Generalized Zero-Shot Learning

https://github.com/uqzhichen/GSMFlow

공식 코드

Mitigating Generation Shifts for Generalized Zero-Shot Learning

https://github.com/uqzhichen/haszsl

공식 코드

Zero-Shot Learning by Harnessing Adversarial Samples

https://github.com/uqzhichen/sdgzsl

공식 코드

Semantics Disentangling for Generalized Zero-Shot Learning

https://github.com/uvavision/tv-gzsl

공식 코드

On the Transferability of Visual Features in Generalized Zero-Shot Learning

https://github.com/valeoai/3DGenZ

공식 코드

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds

https://github.com/ylong4/dfzsl

공식 코드

Data-Free Generalized Zero-Shot Learning

https://github.com/zjrao/aarr

공식 코드

Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning

https://github.com/zjrao/dedn

공식 코드

Dual Expert Distillation Network for Generalized Zero-Shot Learning

https://huggingface.co/geolocal/StreetCLIP

공식 코드

Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

https://github.com/0shimax/Easy-Over-Complete-Distribution

Generalized Zero-Shot Learning Via Over-Complete Distribution

https://github.com/Abhipanda4/Feature-Generating-Networks

Feature Generating Networks for Zero-Shot Learning

https://github.com/Chandan-IITI/Tf-GCZSL

Online Lifelong Generalized Zero-Shot Learning

https://github.com/Chenxingyu1990/A-Boundary-Based-Out-of-Distribution-Classifier-for-Generalized-Zero-Shot-Learning

A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

https://github.com/Chenxingyu1990/gzsl_svae

A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

https://github.com/CristianoPatricio/zsl-methods

Feature Generating Networks for Zero-Shot Learning

https://github.com/Huihui-z/CE-GZSL

Contrastive Embedding for Generalized Zero-Shot Learning

https://github.com/MKYucel/zero_shot_corruption_benchmarks

A Deep Dive into Adversarial Robustness in Zero-Shot Learning

https://github.com/YCAyca/CLIP_ML_Decoder

CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation

https://github.com/akku1506/Feature-Generating-Networks-for-ZSL

Feature Generating Networks for Zero-Shot Learning

https://github.com/mkara44/f-clswgan_pytorch

Feature Generating Networks for Zero-Shot Learning

https://github.com/pujols/Zero-shot-learning-journal

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

https://github.com/sanixa/CADA-VAE-pytorch

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

https://github.com/universome/class-norm

Class Normalization for (Continual)? Generalized Zero-Shot Learning

https://github.com/universome/class-norm-for-czsl

Class Normalization for (Continual)? Generalized Zero-Shot Learning

https://github.com/vkverma01/meta-gzsl

A Meta-Learning Framework for Generalized Zero-Shot Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.