protein-bert-pytorch VS student-teacher-anomaly-detection

Compare protein-bert-pytorch vs student-teacher-anomaly-detection and see what are their differences.


Student–Teacher Anomaly Detection with Discriminative Latent Embeddings (by denguir)
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protein-bert-pytorch student-teacher-anomaly-detection
2 1
65 116
- -
1.0 0.0
11 months ago 9 months ago
Python Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Posts with mentions or reviews of protein-bert-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-08.


Posts with mentions or reviews of student-teacher-anomaly-detection. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-31.

What are some alternatives?

When comparing protein-bert-pytorch and student-teacher-anomaly-detection you can also consider the following projects:

tape - Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

DETReg - Official implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".

image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.

HyperGAN - Composable GAN framework with api and user interface

pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

Fast_Dense_Feature_Extraction - A Pytorch and TF implementation of the paper "Fast Dense Feature Extraction with CNNs with Pooling Layers"

learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)

provis - Official code repository of "BERTology Meets Biology: Interpreting Attention in Protein Language Models."

image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.