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deep-metric-learning
Here are 35 public repositories matching this topic...
Deep Metric Learning
art
dml
image-retrieval
cvpr
xbm
deep-metric-learning
loss-function
multi-similarity-loss
embedding-learning
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Updated
Aug 10, 2020 - Python
PyTorch Implementation for Deep Metric Learning Pipelines
computer-vision
deep-learning
pytorch
neural-networks
metric-learning
deep-metric-learning
cub200
distance-sampling
cars196
pku-vehicle
shop-clothes
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Updated
Jun 17, 2020 - Python
A comprehensive survey of deep metric learning and related works
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Updated
Aug 28, 2020
A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).
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Updated
Feb 6, 2020 - Python
Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020
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Updated
Aug 3, 2020 - Python
(ICML 2020) This repo contains code for our paper "Revisiting Training Strategies and Generalization Performance in Deep Metric Learning" (https://arxiv.org/abs/2002.08473) to facilitate consistent research in this field.
deep-learning
metric-learning
image-retrieval
deep-metric-learning
cars196
wandb
stanford-online-products
cub200-2011
icml2020
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Updated
Aug 25, 2020 - Python
Code for CVPR 2019 paper "Deep Metric Learning to Rank"
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Updated
Apr 4, 2020 - MATLAB
PyTorch implementation of Deep Randomized Ensembles for Metric Learning(ECCV2018)
machine-learning
computer-vision
deep-learning
pytorch
embedding
deep-metric-learning
retrieving-images
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Updated
Jun 15, 2020 - Python
图像检索和向量搜索,similarity learning,compare deep metric and deep-hashing applying in image retrieval
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Updated
May 3, 2020 - Jupyter Notebook
(ICCV 2019) This repo contains code for "MIC: Mining Interclass Characteristics for Improved Metric Learning", which proposes an auxiliary training task to explain away intra-class variations.
pytorch
deep-metric-learning
cub200
deep-lear
shop-clothes-retrieval
computer-vi
cars196
pku-vehicle
metric-
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Updated
Jun 12, 2020 - Python
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
deep-learning
metric-learning
temporal-models
re-identification
image-matching
deep-metric-learning
person-reidentification
person-search
interpretability
generalization
re-id
person-recognition
person-re-identification
correspondence
interpretable-deep-learning
reid
person-reid
adaptive-convolution
person-retrieval
generalizability
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Updated
Aug 12, 2020 - Python
(CVPR 2020) This repo contains code for "PADS: Policy-Adapted Sampling for Visual Similarity Learning", which proposes learnable triplet mining with Reinforcement Learning.
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Updated
Jun 12, 2020 - Python
CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission
learning-to-rank
image-retrieval
deep-metric-learning
image-clustering
fine-grained-recognition
open-set-recognition
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Updated
Aug 24, 2020 - Shell
source code for the paper "Hard-Aware-Deeply-Cascaed-Embedding"
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Updated
Feb 20, 2017 - C++
simple example face recognition with deep metric learning to dlib
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Apr 3, 2017 - Makefile
A simple, modern and scalable facial recognition based attendance system built with Python back-end & Angular front-end.
opencv
flask
sqlalchemy
angular
deep-learning
command-line
numpy
pillow
pandas
python3
attendance
face-recognition
face-detection
dlib
deep-metric-learning
attendance-system
knn-classification
face-recognition-python
face-encodings
facial-embeddings
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Updated
Jul 6, 2020 - Python
Hardness-Aware Deep Metric Learning (CVPR2019) in pytorch
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Updated
Oct 10, 2019 - Python
(Pytorch and Tensorflow) Implementation of Weighted Contrastive Loss (Deep Metric Learning by Online Soft Mining and Class-Aware Attention)
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Oct 21, 2019 - Python
pytorch implement of this paper: https://arxiv.org/abs/1807.11176
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Updated
Feb 16, 2020 - Python
Metric Learning (npair loss & angular loss) on mnist and Visualizing by t_SNE
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Updated
Nov 1, 2019 - Python
SoftTriple (ICCV2019) in pytorch
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Updated
Oct 11, 2019 - Python
Deep Metric Learning by Online Soft Mining and Class-Aware Attention, AAAI 2019 Oral
image-recognition
deep-metric-learning
person-reidentification
video-recognition
video-retrieval
person-retrieval
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Updated
Jul 9, 2020 - Shell
codes for TGRS paper: Graph Relation Network: Modeling Relations between Scenes for Multi-Label Remote Sensing Image Classification and Retrieval
remote-sensing
image-retrieval
deep-metric-learning
multilabel-classification
landcover-classification
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Updated
Aug 22, 2020 - Python
Basic reference for Multi View Classification - mvcnn
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Updated
Jul 21, 2019 - Python
(ECCV 2020) This repo contains code for "DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning" (https://arxiv.org/abs/2004.13458), which extends vanilla DML with auxiliary and self-supervised features.
deep-learning
metric-learning
image-retrieval
deep-metric-learning
cars196
wandb
stanford-online-products
cub200-2011
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Updated
Aug 25, 2020 - Python
Deep metric learning repository
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Updated
Apr 14, 2020 - Python
Replication of 'Deep metric Learning Using Triplet Network'
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Updated
Aug 8, 2020 - Python
codes for RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery
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Updated
Aug 12, 2020 - Python
Minimalistic TensorFlow2+ deep metric/similarity learning library with loss functions, miners, and utils as embedding projector.
python
machine-learning
computer-vision
deep-learning
tensorflow
metric-learning
visual-search
image-retrieval
deep-metric-learning
similarity-search
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Updated
Jul 23, 2020 - Python
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Let's say I set
m=8
andbatch_size=32
, and I have eight classes (A-H). That should mean that each batch is comprised of samples from only four classes, and each class is only represented in a single batch. That would then mean that samples from Class A could be in negative pairs with samples from Classes B-D, but never with samples from Classes E-H. Is that all correct?How then does this i