https://github.com/nktoan/causal-inference-via-style-transfer-for-ood-generalisation
공식 코드Causal Inference via Style Transfer for Out-of-distribution Generalisation
Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nkusyl/evit
공식 코드EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention
https://github.com/nlokeshiisc/sfedavg-aaai21
공식 코드Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
https://github.com/nmndeep/robust-segmentation
공식 코드Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
https://github.com/nneurotransmitterr/tm-otta-snn
공식 코드Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
https://github.com/nntanaka/Fourier-Analysis-for-Skeleton-based-Action-Recognition
공식 코드https://github.com/noamgot/DecisioNet
공식 코드https://github.com/northsummer/lgkd
공식 코드BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
https://github.com/nourhanb/biaspruner
공식 코드BiasPruner: Debiased Continual Learning for Medical Image Classification
https://github.com/nphdang/fs-bbt
공식 코드https://github.com/nraghuraman/bongard-context
공식 코드https://github.com/nshaud/DeepHyperX
공식 코드Deep Learning for Classification of Hyperspectral Data: A Comparative Review
https://github.com/ntomen/deep-continuous-networks
공식 코드https://github.com/nupurkmr9/S2M2_fewshot
공식 코드Charting the Right Manifold: Manifold Mixup for Few-shot Learning
https://github.com/nuric/pix2rule
공식 코드https://github.com/nurikorhan/negotiated-representations-for-continual-learning
공식 코드Negotiated Representations to Prevent Forgetting in Machine Learning Applications
https://github.com/nus-tim/cect
공식 코드CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classification
https://github.com/nverma1/merging-ffs-compression
공식 코드https://github.com/nvlabs/bongard-hoi
공식 코드Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
https://github.com/nvlabs/fan
공식 코드https://github.com/nvlabs/gcvit
공식 코드https://github.com/nvlabs/mambavision
공식 코드https://github.com/nyquixt/cascadetta
공식 코드Adaptive Cascading Network for Continual Test-Time Adaptation
https://github.com/nyu-dice-lab/privit
공식 코드https://github.com/nzwang/wsebp
공식 코드https://github.com/o4lc/greg-ood
공식 코드https://github.com/obiyoag/crl
공식 코드https://github.com/odegeasslbc/FastGAN-pytorch
공식 코드Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition
https://github.com/ofa-sys/chinese-clip
공식 코드Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
https://github.com/ofa-sys/ofa
공식 코드https://github.com/ofsoundof/Heterogeneity_Hypothesis
공식 코드The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
https://github.com/ofsoundof/LocalViT
공식 코드https://github.com/ofsoundof/dhp
공식 코드https://github.com/ofsoundof/learning_filter_basis
공식 코드Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ojss/c3lr
공식 코드https://github.com/ojss/samptransfer
공식 코드Self-Attention Message Passing for Contrastive Few-Shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.