Papers with Code 아카이브

Inductive Bias

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

https://github.com/MindSpore-scientific-2/code-11/tree/main/Simba

SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series

https://github.com/MindSpore-scientific/code-1/tree/main/PCGrad-mindspore-example

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

https://github.com/MindSpore-scientific/code-3/tree/main/alibi

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

https://github.com/MindSpore-scientific/code-4/tree/main/PCGrad-mindspore-example

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

https://github.com/MindSpore-scientific/code-8/tree/main/PCGrad-mindspore-example

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

https://github.com/MohammadRezaQaderi/Video-Swin-Transformer

Video Swin Transformer

https://github.com/MostafaDehghani/bAbI-T2T

Universal Transformers

https://github.com/NVIDIA/GraphQSat

Relational inductive biases, deep learning, and graph networks

https://github.com/Natooz/MidiTok

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

https://github.com/PaddlePaddle/PaddleSeg

nnFormer: Interleaved Transformer for Volumetric Segmentation

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md

Video Swin Transformer

https://github.com/PaulaMihalcea/Neural3DMM_BU3DFE

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

https://github.com/QuangNguyen2609/ARCHITECTURAL-BACKDOORS-IN-NEURAL-NETWORKS

Architectural Backdoors in Neural Networks

https://github.com/Recognito-Vision/Face-SDK-Linux-Demos

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

https://github.com/RobvanGastel/meta-in-context-learning

General-Purpose In-Context Learning by Meta-Learning Transformers

https://github.com/RongKaiWeskerMA/INSTA

Prototypical Networks for Few-shot Learning

https://github.com/Rowing0914/Graph_Nets

Relational inductive biases, deep learning, and graph networks

https://github.com/SforAiDl/vformer

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

https://github.com/Sha-Lab/FEAT

Prototypical Networks for Few-shot Learning

https://github.com/SimonKohl/probabilistic_unet

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

https://github.com/SongweiGe/shift-invariance-adv-robustness

Towards Learning Convolutions from Scratch

https://github.com/SunghwanHong/Cost-Aggregation-transformers

Cost Aggregation Is All You Need for Few-Shot Segmentation

https://github.com/TQCAI/graph_nets_pytorch

Relational inductive biases, deep learning, and graph networks

https://github.com/The-AI-Summer/self-attention-cv

Convolution-Free Medical Image Segmentation using Transformers

https://github.com/TieDanCuihua/Hierarchical-Attention-Networks-for-Document-Classification-Tensorflow

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

https://github.com/TieDanCuihua/ORDERED-NEURONS-INTEGRATING-TREE-STRUCTURES-INTO-RECURRENT-NEURAL-NETWORKS--tensorflow

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

https://github.com/TsingZ0/GFL

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

https://github.com/TsingZ0/PFL-Non-IID

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

https://github.com/VICO-UoE/KD4MTL

Multi-Task Learning as Multi-Objective Optimization

https://github.com/VahidZee/nads

Neural Anisotropy Directions

https://github.com/ViTAE-Transformer/ViTDet

ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond

https://github.com/WESTENR-OU/S4D_rainfall_runoff_simulations

MC-LSTM: Mass-Conserving LSTM

https://github.com/WangTianduo/Prototypical-Networks

Prototypical Networks for Few-shot Learning

https://github.com/Westlake-AI/openmixup

RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer

https://github.com/Whiffe/mmaction2_YF

Video Swin Transformer

https://github.com/YilinLiu97/Faster-DIP-Recon

Deep Image Prior

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