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Inductive Bias

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

https://github.com/cheezzjazz/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/chinhsuanwu/coatnet-pytorch

CoAtNet: Marrying Convolution and Attention for All Data Sizes

https://github.com/cnguyen10/few_shot_meta_learning

Prototypical Networks for Few-shot Learning

https://github.com/cnielly/prototypical-networks-omniglot

Prototypical Networks for Few-shot Learning

https://github.com/code-implementation1/Code6/tree/main/PatchCore

Optimizing PatchCore for Few/many-shot Anomaly Detection

https://github.com/code-implementation1/Code8/tree/main/snn_mlp

Brain-inspired Multilayer Perceptron with Spiking Neurons

https://github.com/code-implementation1/Code9/tree/main/VIT

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/collabora/whisperspeech

Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis

https://github.com/conceptofmind/ViT-Small-Datasets-flax

Vision Transformer for Small-Size Datasets

https://github.com/cumlsec/stateformer

Neural Arithmetic Units

https://github.com/cyvius96/prototypical-network-pytorch

Prototypical Networks for Few-shot Learning

https://github.com/cyye001/con-cdvae

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

https://github.com/daeheepark/t4p

Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

https://github.com/davidtangGT/MEGNET

Relational inductive biases, deep learning, and graph networks

https://github.com/dcccc/LC_NET

Relational inductive biases, deep learning, and graph networks

https://github.com/dcccc/git_python

Relational inductive biases, deep learning, and graph networks

https://github.com/deepmind/jraph

Relational inductive biases, deep learning, and graph networks

https://github.com/dmeoli/neuro-sat

Relational inductive biases, deep learning, and graph networks

https://github.com/dniku/perceptual-gradient-networks

Deep Image Prior

https://github.com/dome272/VQGAN

Taming Transformers for High-Resolution Image Synthesis

https://github.com/dome272/vqgan-pytorch

Taming Transformers for High-Resolution Image Synthesis

https://github.com/drv-agwl/Implicit_Neural_Representation

Phase Transitions, Distance Functions, and Implicit Neural Representations

https://github.com/dslisleedh/MLP_based_models-flax/blob/main/s2mlpv2.py

S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision

https://github.com/duanyzhi/graph_net

Relational inductive biases, deep learning, and graph networks

https://github.com/ebadrian/metadl

Prototypical Networks for Few-shot Learning

https://github.com/ebagdasa/backdoors101

Multi-Task Learning as Multi-Objective Optimization

https://github.com/edbeeching/learning_to_plan

Learning to plan with uncertain topological maps

https://github.com/elieattias1/pixel-reg

Learning From Brains How to Regularize Machines

https://github.com/fabian57fabian/prototypical-networks-few-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/facebookresearch/DomainBed

Reducing Domain Gap by Reducing Style Bias

https://github.com/facebookresearch/SlowFast

Masked Autoencoders As Spatiotemporal Learners

https://github.com/facebookresearch/disentangling-correlated-factors

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

https://github.com/facebookresearch/flowmm

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

https://github.com/facebookresearch/minihack

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

https://github.com/facebookresearch/vissl

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

https://github.com/fxia22/gn.pytorch

Graph networks as learnable physics engines for inference and control

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