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adaptive-da-attack

Implement of An Imperceptible Data Augmentation Based Blackbox Clean-Label Backdoor Attack on Deep Neural Networks

annotated_deep_learning.paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

awesome-automl-and-lightweight-models

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

axial-attention

Implementation of Axial attention - attending to multi-dimensional data efficiently

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bedn

Implement of Backdoor Attack on Deep Learning-Based Medical Image Encryption and Decryption Network

bertandresnet50

BERT and ResNet50

blocksparse

Efficient GPU kernels for block-sparse matrix multiplication and convolution

bolt

10x faster matrix and vector operations.

cca_ud-universal-training-level-defence

Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis

cheetah-texture-packer

High efficient and fast 2D bin packing tool

circumventing-backdoor-defenses

Revisiting the Assumption of Latent Separability for Backdoor Defenses

classification_models

Classification models trained on ImageNet. Keras.

clrs

:notebook:Solutions to Introduction to Algorithms

clusternet

Code release for NeurIPS 2019 paper "End to End Learning and Optimization on Graphs"

coin-or-optimizationsuite

A harness for building the bundled suite of interoperable optimization tools available in the COIN-OR repository.

computational_efficient_backdoor

Computation and Data Efficient Backdoor Attacks

convnets-tensorflow2

⛵️ Implementation a variety of popular Image Classification Models using TensorFlow2. [ResNet, GoogLeNet, VGG, Inception-v3, Inception-v4, MobileNet, MobileNet-v2, ShuffleNet, ShuffleNet-v2, etc...]

dca

Dynamic channel allocation in cellular networks by reinforcement learning

deep-architectures-for-modulation-recognition

deep learning implement for modulation classification ,codes for paper <Deep Architectures for Modulation Recognition>

deep-compression-alexnet

Deep Compression on AlexNet

deep-compression-pytorch

PyTorch implementation of 'Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding' by Song Han, Huizi Mao, William J. Dally

deep-neural-network-architectures-for-modulation-classification

code for Deep Neural Network Architectures for Modulation Classification

deep-reinforcement-learning-algorithms-with-pytorch

PyTorch implementations of deep reinforcement learning algorithms and environments

deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

deep-tempest

Restoration for TEMPEST images using deep-learning

deepcompression-caffe

Caffe for Deep Compression

deepgradientcompression

It is implementation of Research paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING". Deep gradient compression is a technique by which the gradients are compressed before they are being sent. This approach greatly reduces the communication bandwidth and thus improves multi node training.