S2-BNN
Unsupervised-Classification
| S2-BNN | Unsupervised-Classification | |
|---|---|---|
| 1 | 2 | |
| 61 | 1,309 | |
| - | - | |
| 2.7 | 1.4 | |
| over 2 years ago | 10 months ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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S2-BNN
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[R] Knowledge Distillation, Model Ensemble and Its Application on Visual Recognition - Link to a free online lecture by the author in comments
CVPR 2021 paper ‘S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration’, Github: https://github.com/szq0214/S2-BNN
Unsupervised-Classification
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Middle ground dataset between CIFAR and ImageNet [D]
The subsets we used are from here: https://github.com/wvangansbeke/Unsupervised-Classification/tree/master/data/imagenet_subsets
- Any reference or idea about how to train unsupervised CNN model ?
What are some alternatives?
temporal-shift-module - [ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
MEAL-V2 - MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.
self-supervised - Whitening for Self-Supervised Representation Learning | Official repository
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
contrastive-reconstruction - Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"
Transformer-SSL - This is an official implementation for "Self-Supervised Learning with Swin Transformers".
vectorrvnn - Data Driven method for hierarchical grouping of paths in Vector Graphics.
cs231n
SimMIM - This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".
PASS - The PASS dataset: pretrained models and how to get the data