Papers with Code 아카이브

Knowledge Distillation

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하위 태스크: 2
평가표: 11
보존된 평가 기록: 90

Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.

코드

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

https://github.com/kinalmehta/epymarl

The StarCraft Multi-Agent Challenge

https://github.com/kinit-sk/imgtb

MGTBench: Benchmarking Machine-Generated Text Detection

https://github.com/kinshukdua/FedUKD

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

https://github.com/kirangpcet/KRCCTumor

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/kirubarajan/roft

Automatic Detection of Generated Text is Easiest when Humans are Fooled

https://github.com/kirubarajan/trick

Automatic Detection of Generated Text is Easiest when Humans are Fooled

https://github.com/kiyoon/pyvideoai

ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

https://github.com/kl-lab/fuma

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

https://github.com/kl-lab/gratis

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

https://github.com/klemens-floege/oneprot

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/knotgrass/Knowledge-Distillation

Distilling the Knowledge in a Neural Network

https://github.com/knuddj1/op_text

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/knuddy/op_text

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/komyeongjin/specdiff-gan

FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

https://github.com/kongzhiyou/darknet-master

Focal Loss for Dense Object Detection

https://github.com/konyul/mmdet3d

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/kornia/kornia

Focal Loss for Dense Object Detection

https://github.com/korotulea/Quantifying-Hippocampus-Volume-for-Alzheimers-Progression-AI-Assistant

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

https://github.com/kostrzmar/SATEF

Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

https://github.com/krantirk/BERT-Pretrained-model

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/ktw361/mmdetection_impl

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/kuangliu/pytorch-fpn

Focal Loss for Dense Object Detection

https://github.com/kvijaykrish/Bosch_AI_VehicleClassification

Focal Loss for Dense Object Detection

https://github.com/kylrth/procedure-generation

I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

https://github.com/kynkaat/role-of-imagenet-classes-in-fid

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/kyunghwan/darknet_v3

Focal Loss for Dense Object Detection

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Distilling the Knowledge in a Neural Network

https://github.com/lackel/hierarchical_weighted_scl

Benchmarking Natural Language Understanding Services for building Conversational Agents

https://github.com/lackel/sdc

Benchmarking Natural Language Understanding Services for building Conversational Agents

https://github.com/lamalab-org/mac-bench

Are large language models superhuman chemists?

https://github.com/laurendelong21/mars

Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs

https://github.com/lavoiems/Cats-UDT

Moment Matching for Multi-Source Domain Adaptation

https://github.com/lavoiems/SADt

Moment Matching for Multi-Source Domain Adaptation

https://github.com/lavoiems/SPUDT

Moment Matching for Multi-Source Domain Adaptation

https://github.com/ldkong1205/PointCloud-C

Benchmarking and Analyzing Point Cloud Classification under Corruptions

https://github.com/lee-zq/3DUNet-Pytorch

The Liver Tumor Segmentation Benchmark (LiTS)

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