PyramidnetTorch implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Stars: ✭ 121 (+572.22%)
Mutual labels: imagenet, torch7
Video-Compression-NetA new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole…
Stars: ✭ 20 (+11.11%)
Mutual labels: autoencoder
BottleneckTransformersBottleneck Transformers for Visual Recognition
Stars: ✭ 231 (+1183.33%)
Mutual labels: imagenet
Continuous-Image-AutoencoderDeep learning image autoencoder that not depends on image resolution
Stars: ✭ 20 (+11.11%)
Mutual labels: autoencoder
SAN[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images
Stars: ✭ 41 (+127.78%)
Mutual labels: imagenet
SharpPeleeNetImageNet pre-trained SharpPeleeNet can be used in real-time Semantic Segmentation/Objects Detection
Stars: ✭ 13 (-27.78%)
Mutual labels: imagenet
GATEThe implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"
Stars: ✭ 65 (+261.11%)
Mutual labels: autoencoder
probabilistic nlgTensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
Stars: ✭ 28 (+55.56%)
Mutual labels: autoencoder
Face-LandmarkingReal time face landmarking using decision trees and NN autoencoders
Stars: ✭ 73 (+305.56%)
Mutual labels: autoencoder
peaxPeax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Stars: ✭ 63 (+250%)
Mutual labels: autoencoder
ImageModelsImageNet model implemented using the Keras Functional API
Stars: ✭ 63 (+250%)
Mutual labels: imagenet
SESF-FuseSESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
Stars: ✭ 47 (+161.11%)
Mutual labels: autoencoder
PyTorch-LMDBScripts to work with LMDB + PyTorch for Imagenet training
Stars: ✭ 49 (+172.22%)
Mutual labels: imagenet
Unsupervised Deep LearningUnsupervised (Self-Supervised) Clustering of Seismic Signals Using Deep Convolutional Autoencoders
Stars: ✭ 36 (+100%)
Mutual labels: autoencoder
topological-autoencodersCode for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
Stars: ✭ 82 (+355.56%)
Mutual labels: autoencoder
Stochastic-QuantizationTraining Low-bits DNNs with Stochastic Quantization
Stars: ✭ 70 (+288.89%)
Mutual labels: imagenet
