https://github.com/google-research/noisy-fewshot-learning
공식 코드Graph convolutional networks for learning with few clean and many noisy labels
Papers with Code 아카이브
Few-Shot Learning
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Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/prompt-tuning
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/tasseo
공식 코드https://github.com/google-research/vmf_embeddings
공식 코드von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
https://github.com/google-research/vmoe
공식 코드https://github.com/google/example_extrapolation
공식 코드https://github.com/graphmolab/graph2token
공식 코드https://github.com/griffbr/tfod
공식 코드https://github.com/gtzheng/fewstab
공식 코드https://github.com/guangxinghan/meta-faster-r-cnn
공식 코드Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
https://github.com/guoqianyu-alberta/rankdnn
공식 코드https://github.com/guybuk/ANCOR
공식 코드Fine-grained Angular Contrastive Learning with Coarse Labels
https://github.com/gy20073/compact_bilinear_pooling
공식 코드https://github.com/haamoon/finding_common_object
공식 코드Learning to Find Common Objects Across Few Image Collections
https://github.com/hadijomaa/dataset2vec
공식 코드https://github.com/hadjisma/VideoAlignment
공식 코드Representation Learning via Global Temporal Alignment and Cycle-Consistency
https://github.com/haidongz-usc/CaesarNeRF
공식 코드CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
https://github.com/halo2tang/code-for-lwfm-spm
공식 코드Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
https://github.com/han-jia/lastshot
공식 코드https://github.com/hanlu-nju/revisiting-uml
공식 코드Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
https://github.com/haoheliu/dcase_2022_task_5
공식 코드Segment-level Metric Learning for Few-shot Bioacoustic Event Detection
https://github.com/haolsun/api
공식 코드https://github.com/haoni0812/mda
공식 코드https://github.com/haotianxia/SPORTU
공식 코드SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models
https://github.com/haotianxia/sportqa
공식 코드SportQA: A Benchmark for Sports Understanding in Large Language Models
https://github.com/hazyresearch/h3
공식 코드Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/hc495/ICL_Circuit
공식 코드Revisiting In-context Learning Inference Circuit in Large Language Models
https://github.com/hccngu/meta-sn
공식 코드Meta-Learning Siamese Network for Few-Shot Text Classification
https://github.com/heekhero/ACSR
공식 코드https://github.com/heethanjan/Feature-Generator-for-FSL
공식 코드https://github.com/henulwy/stbdip
공식 코드Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
https://github.com/heqin-zhu/uod_universal_oneshot_detection
공식 코드https://github.com/hertin/wavprompt
공식 코드WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models
https://github.com/hexiaoxiao-cs/fedml-knee
공식 코드https://github.com/hhhhedy/gfss-ekt
공식 코드Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer
https://github.com/hitachi-nlp/backward-llm
공식 코드Acquiring Bidirectionality via Large and Small Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.