Papers with Code 아카이브

Auxiliary Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.

<span style="color:grey; opacity: 0.6">( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )</span>

코드

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

https://github.com/Algolzw/SPAC-Deformable-Registration

공식 코드

RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning

https://github.com/AvivNavon/AuxiLearn

공식 코드

Auxiliary Learning by Implicit Differentiation

https://github.com/AvivSham/auxinash

공식 코드

Auxiliary Learning as an Asymmetric Bargaining Game

https://github.com/adaptive-agents-lab/understading_auxilliary_tasks

공식 코드

When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning

https://github.com/dong-huo/srr-maxl

공식 코드

Learning to Recover Spectral Reflectance from RGB Images

https://github.com/ethanygao/aux-nas

공식 코드

Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost

https://github.com/hadijomaa/dataset2vec

공식 코드

Dataset2Vec: Learning Dataset Meta-Features

https://github.com/hamanhbui/reliable_ssl_baselines

공식 코드

Benchmark for Uncertainty & Robustness in Self-Supervised Learning

https://github.com/hassounlab/boost-rs

공식 코드

Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction

https://github.com/joel99/objectnav

공식 코드

Auxiliary Tasks and Exploration Enable ObjectNav

https://github.com/ldery/automating-auxiliary-learning

공식 코드

AANG: Automating Auxiliary Learning

https://github.com/lesley222/HJAILab-OPEN

공식 코드

RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning

https://github.com/lorenmt/auto-lambda

공식 코드

Auto-Lambda: Disentangling Dynamic Task Relationships

https://github.com/lorenmt/maxl

공식 코드

Self-Supervised Generalisation with Meta Auxiliary Learning

https://github.com/mlvlab/MELTR

공식 코드

MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models

https://github.com/mlvlab/SELAR

공식 코드

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

https://github.com/plrbear/auxskd

공식 코드

Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation

https://github.com/pykt-team/pykt-toolkit

공식 코드

Enhancing Deep Knowledge Tracing with Auxiliary Tasks

https://github.com/siyiwu0330/SDF-TopoNet

공식 코드

SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning

https://github.com/smallmax00/counting_with_adaptive_auxiliary

공식 코드

Counting with Adaptive Auxiliary Learning

https://github.com/vishaldeyiiest/graphta

공식 코드

Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation

https://github.com/wujiangxu/amid

공식 코드

A Survey on Cross-Domain Sequential Recommendation

https://github.com/xmeimeimei/sgtc

공식 코드

SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation

https://github.com/xulianuwa/AuxSegNet

공식 코드

Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation

https://github.com/yoqim/pose_vireid

공식 코드

On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification

https://github.com/zhangshengjun2019/geoauxnet

공식 코드

GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds

https://github.com/COMP6248-Reproducability-Challenge/COMP6248-Self-Supervised-Generalisation-with-Meta-Auxiliary-Learning

Self-Supervised Generalisation with Meta Auxiliary Learning

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