https://github.com/event-ahu/coesot
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
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/event-ahu/eventvot_benchmark
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
https://github.com/event-ahu/openpar
공식 코드SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm
https://github.com/evolvinglmms-lab/lmms-eval
공식 코드https://github.com/ewsheng/nlg-bias
공식 코드The Woman Worked as a Babysitter: On Biases in Language Generation
https://github.com/exgc/avmust-ted
공식 코드https://github.com/exoskelebox/force-myography-hand-gesture-recognition-benchmark-data
공식 코드Force myography benchmark data for hand gesture recognition and transfer learning
https://github.com/expertailab/capturing-pertinent-symbolic-features-for-enhanced-content-based-misinformation-detection
공식 코드Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection
https://github.com/expertailab/spaceqa
공식 코드SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts
https://github.com/explainableml/general-knowledge-transfer
공식 코드https://github.com/exploration-lab/hldc
공식 코드https://github.com/exploration-lab/iitk-semeval-2024-task-4-pursuasion-techniques
공식 코드IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
https://github.com/exploration-lab/mutec
공식 코드Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
https://github.com/ezzzli/prompt-pate
공식 코드Exploring the Benefits of Visual Prompting in Differential Privacy
https://github.com/fabianraisch/GenTL
공식 코드GenTL: A General Transfer Learning Model for Building Thermal Dynamics
https://github.com/fabiopoiesi/monet_dataset
공식 코드The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
https://github.com/fabprezja/deep-fast-vision
공식 코드Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision Prototyping
https://github.com/facebookresearch/GraphLog
공식 코드https://github.com/facebookresearch/InferSent
공식 코드Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/facebookresearch/PointContrast
공식 코드PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
https://github.com/facebookresearch/SWAG
공식 코드Revisiting Weakly Supervised Pre-Training of Visual Perception Models
https://github.com/facebookresearch/SentEval
공식 코드Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/facebookresearch/Zero-Shot-DST
공식 코드Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
https://github.com/facebookresearch/adatt
공식 코드AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
https://github.com/facebookresearch/agem
공식 코드https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/classifier-balancing
공식 코드Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/facebookresearch/deit
공식 코드https://github.com/facebookresearch/heterogeneous_botl
공식 코드Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
https://github.com/facebookresearch/llama
공식 코드https://github.com/facebookresearch/llama-hd-dataset
공식 코드https://github.com/facebookresearch/metaicl
공식 코드https://github.com/facebookresearch/minihack
공식 코드MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
https://github.com/facebookresearch/mmf/tree/main/projects/unit
공식 코드UniT: Multimodal Multitask Learning with a Unified Transformer
https://github.com/facebookresearch/mtenv
공식 코드Multi-Task Reinforcement Learning with Context-based Representations
https://github.com/facebookresearch/mtrl
공식 코드Multi-Task Reinforcement Learning with Context-based Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.