https://github.com/minhlong94/random-mixup
공식 코드Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Papers with Code 아카이브
Image Classification
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- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/minhtannguyen/SRSGD
공식 코드Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
https://github.com/minimalparts/nonce2vec
공식 코드High-risk learning: acquiring new word vectors from tiny data
https://github.com/minogame/caffe-qhconv
공식 코드Design of Kernels in Convolutional Neural Networks for Image Classification
https://github.com/minoskt/PoliBox
공식 코드https://github.com/minoskt/PoliFL
공식 코드https://github.com/minyoungg/overparam
공식 코드https://github.com/miquelmarti/DenseFixMatch
공식 코드An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
https://github.com/mir-aidj/edm-subgenre-classifier
공식 코드Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
https://github.com/misterpawan/dycent
공식 코드https://github.com/mit-futuretech/thecomputationallimitsofdeeplearning
공식 코드https://github.com/mit-han-lab/efficientvit
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/mit-han-lab/torchquantum
공식 코드QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
https://github.com/mit-ll-responsible-ai/equine-webapp
공식 코드ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
https://github.com/mitscha/strassennets
공식 코드https://github.com/mitsuichen14/lrsclip
공식 코드LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
https://github.com/mkhodak/relax
공식 코드https://github.com/ml-jku/EVA
공식 코드Parameter Efficient Fine-tuning via Explained Variance Adaptation
https://github.com/ml-jku/MIM-Refiner
공식 코드MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
https://github.com/ml-jku/hopfield-boosting
공식 코드Energy-based Hopfield Boosting for Out-of-Distribution Detection
https://github.com/ml-jku/mae-ct
공식 코드Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
https://github.com/ml-research/learning-by-self-explaining
공식 코드https://github.com/ml-research/rational_activations
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/ml-research/rational_rl
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/ml-research/rational_sl
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/ml4ai/mliis
공식 코드https://github.com/mlapin/libsdca
공식 코드https://github.com/mlbio-epfl/crow
공식 코드https://github.com/mlbio-epfl/falcon
공식 코드https://github.com/mlbio-epfl/turtle
공식 코드https://github.com/mlcommons/tiny
공식 코드https://github.com/mlcommons/tiny_results_v0.5
공식 코드https://github.com/mlcommons/tiny_results_v0.7/tree/main/open/hls4ml-finn
공식 코드https://github.com/mlech26l/learning-long-term-irregular-ts
공식 코드Learning Long-Term Dependencies in Irregularly-Sampled Time Series
https://github.com/mlech26l/ode-lstms
공식 코드Learning Long-Term Dependencies in Irregularly-Sampled Time Series
https://github.com/mlfoundations/imagenet-captions
공식 코드Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.