https://github.com/explainableml/bayescap
공식 코드BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
Papers with Code 아카이브
2D Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 27
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/explosion5/Dolphins
공식 코드DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
https://github.com/f90/AdversarialAudioSeparation
공식 코드Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
https://github.com/f90/Wave-U-Net
공식 코드Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
https://github.com/fabiankueppers/calibration-framework
공식 코드https://github.com/fabienbaradel/object_level_visual_reasoning
공식 코드https://github.com/fabiencode/lg3d
공식 코드https://github.com/fabiopoiesi/monet_dataset
공식 코드The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
https://github.com/fabiotosi92/nerf-supervised-deep-stereo
공식 코드https://github.com/facebookresearch/AnimalAvatar
공식 코드Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
https://github.com/facebookresearch/ConvNeXt
공식 코드https://github.com/facebookresearch/DepthContrast
공식 코드Self-Supervised Pretraining of 3D Features on any Point-Cloud
https://github.com/facebookresearch/Detic
공식 코드Detecting Twenty-thousand Classes using Image-level Supervision
https://github.com/facebookresearch/DrQA
공식 코드https://github.com/facebookresearch/LASER
공식 코드WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
https://github.com/facebookresearch/MLQA
공식 코드MLQA: Evaluating Cross-lingual Extractive Question Answering
https://github.com/facebookresearch/NeuralPassthrough
공식 코드https://github.com/facebookresearch/PLRC
공식 코드Point-Level Region Contrast for Object Detection Pre-Training
https://github.com/facebookresearch/SlowFast
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/adaptive_teacher
공식 코드https://github.com/facebookresearch/barlowtwins
공식 코드Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/facebookresearch/co3d
공식 코드Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
https://github.com/facebookresearch/convnext-v2
공식 코드ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
https://github.com/facebookresearch/cutler
공식 코드Cut and Learn for Unsupervised Object Detection and Instance Segmentation
https://github.com/facebookresearch/deit
공식 코드Augmenting Convolutional networks with attention-based aggregation
https://github.com/facebookresearch/demucs
공식 코드https://github.com/facebookresearch/detectron
공식 코드https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet
공식 코드Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/facebookresearch/detectron2/tree/master/projects/TensorMask
공식 코드https://github.com/facebookresearch/detr
공식 코드https://github.com/facebookresearch/dino
공식 코드https://github.com/facebookresearch/efm3d
공식 코드EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
https://github.com/facebookresearch/egoobjects
공식 코드EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
https://github.com/facebookresearch/fair_self_supervision_benchmark
공식 코드Scaling and Benchmarking Self-Supervised Visual Representation Learning
https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.