https://github.com/MindSpore-scientific/code-13/tree/main/MicAugment
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
Circle Loss: A Unified Perspective of Pair Similarity Optimization
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
NetVLAD: CNN architecture for weakly supervised place recognition
STIR: Siamese Transformer for Image Retrieval Postprocessing
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Learning a Representation for Cover Song Identification Using Convolutional Neural Network
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
Linear Algebraic Structure of Word Senses, with Applications to Polysemy
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
Combination of Multiple Global Descriptors for Image Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.