Papers with Code 아카이브
Transfer Learning
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Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aliprf/ASMNet
공식 코드ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
https://github.com/alireza-nasiri/SoundCLR
공식 코드SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
https://github.com/allenai/better-promptability
공식 코드Continued Pretraining for Better Zero- and Few-Shot Promptability
https://github.com/allenai/rainbow
공식 코드UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
https://github.com/allenai/unifiedqa
공식 코드UnifiedQA: Crossing Format Boundaries With a Single QA System
https://github.com/allminerlab/graphlora
공식 코드GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
https://github.com/alonso130r/knowledge-distillation
공식 코드Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting
https://github.com/alpk/tid-supervised-transfer-learning-dataset
공식 코드Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
https://github.com/alsabahi2030/mh-gec
공식 코드Multi-head Sequence Tagging Model for Grammatical Error Correction
https://github.com/alsabahi2030/synthetic-data-generation
공식 코드Multi-head Sequence Tagging Model for Grammatical Error Correction
https://github.com/alteia-ai/icss
공식 코드Weakly-supervised continual learning for class-incremental segmentation
https://github.com/altsoph/EENLP
공식 코드https://github.com/alvinhan123/ldmlr
공식 코드https://github.com/alx87grd/dimensionlesspolicies
공식 코드https://github.com/alznn/taiwanese-hokkien_mandarin_cm_dataset
공식 코드https://github.com/amaai-lab/muvi
공식 코드https://github.com/amaai-lab/sonicverse
공식 코드SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning
https://github.com/amarasovic/naacl-mpqa-srl4orl
공식 코드SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling
https://github.com/amartinez2020/COVID_CT
공식 코드Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning
https://github.com/amazon-research/assaying-ood
공식 코드Assaying Out-Of-Distribution Generalization in Transfer Learning
https://github.com/amazon-research/replay-based-recurrent-rl
공식 코드Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amazon-science/gnn-tail-generalization
공식 코드You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
https://github.com/amazon-science/instruction-tuning-for-absa
공식 코드Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
https://github.com/amazon-science/replay-based-recurrent-rl
공식 코드Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
https://github.com/ambekarsameer96/skdcgn
공식 코드SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs
https://github.com/amiiir-sarfi/seal
공식 코드Simulated Annealing in Early Layers Leads to Better Generalization
https://github.com/aminK8/TaxaDiffusion
공식 코드TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
https://github.com/aminbana/MTL-Theory
공식 코드Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning
https://github.com/amirveyseh/definition_extraction
공식 코드A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
https://github.com/amkatrutsa/edm
공식 코드Follow the bisector: a simple method for multi-objective optimization
https://github.com/ammarsherif/stg-mtl
공식 코드STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
https://github.com/amnonattali/dsaa
공식 코드Discrete State-Action Abstraction via the Successor Representation
https://github.com/amrnag/specialized-transformers
공식 코드https://github.com/amzn/xfer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.