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Self-Learning

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

https://github.com/oneThousand1000/3DPortraitGAN

공식 코드

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

https://github.com/patrik-ha/explainable-minichess

공식 코드

Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts

https://github.com/petrosgk/L-Game-DQN

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A Deep Q-Learning Agent for the L-Game with Variable Batch Training

https://github.com/polyusmart/doctor-recommendation

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Doctor Recommendation in Online Health Forums via Expertise Learning

https://github.com/pooyanjamshidi/ElasticBench

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Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

https://github.com/purduedigitaltwin/vit-dd

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ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection

https://github.com/rain-neuromorphics/energy-based-learning

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A universal approximation theorem for nonlinear resistive networks

https://github.com/realgump/mvmhat

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Unveiling the Power of Self-supervision for Multi-view Multi-human Association and Tracking

https://github.com/sara-ahmed/gmml

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GMML is All you Need

https://github.com/sbelharbi/deep-active-learning-for-joint-classification-and-segmentation-with-weak-annotator

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Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

https://github.com/schuderer/bprl

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Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models

https://github.com/sheng-eatamath/s3a

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Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

https://github.com/siyuyuan/scua

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Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?

https://github.com/snap-research/mmvid

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Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning

https://github.com/snu-list/deeprf

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Deep reinforcement learning-designed radiofrequency waveform in MRI

https://github.com/stasl0217/KEnS

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Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

https://github.com/susaha/ctrl-uda

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Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation

https://github.com/swook/faze_preprocess

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Self-Learning Transformations for Improving Gaze and Head Redirection

https://github.com/tansq/MCTSteg

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MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography

https://github.com/teddy-f-47/self-learning-llm-public

공식 코드

Into the Unknown: Self-Learning Large Language Models

https://github.com/thunlp/Knowledge-Inheritance

공식 코드

Knowledge Inheritance for Pre-trained Language Models

https://github.com/thunlp/SICOG

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Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition

https://github.com/thunlp/kbalign

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KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

https://github.com/tiancuteqy/endoomni

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EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels

https://github.com/tongkunguan/ccd

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Self-supervised Character-to-Character Distillation for Text Recognition

https://github.com/tranngocduvnvp/litenext

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LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation

https://github.com/trust-tuda/argus-data

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ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks

https://github.com/venturi123/drlinfluids

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Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

https://github.com/victorlopezpastor/self_learning_machines

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Self-learning Machines based on Hamiltonian Echo Backpropagation

https://github.com/wkklavis/dapsam

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Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation

https://github.com/wzh-nlp/www25-cmas

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A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition

https://github.com/yetianjhu/alphallm

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Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

https://github.com/youngryan1993/PrDA-Progressive-Domain-Adaptation-from-a-Source-Pre-trained-Model

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Domain Adaptation without Source Data

https://github.com/youngryan1993/SFDA-Domain-Adaptation-without-Source-Data

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Domain Adaptation without Source Data

https://github.com/youngryan1993/sfda-sourcefreeda

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Domain Adaptation without Source Data

https://github.com/yutinghe-list/cover

공식 코드

Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision

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