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ml-lab's Projects

searl icon searl

Sample-Efficient Automated Deep Reinforcement Learning

sec icon sec

Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

sedlm icon sedlm

Language modelling for sound event detection

sedst icon sedst

code for CIKM'18 long paper, Explicit state tracking with semi-supervision for neural dialogue generation

seed_rl icon seed_rl

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

seeing-red icon seeing-red

Using PPG Obtained via Smartphone Cameras for Authentication

seer icon seer

Time series forecasting microservice

seg_every_thing icon seg_every_thing

Code release for R. Hu et al., Learning to Segment Every Thing. in CVPR, 2018.

segan_pytorch icon segan_pytorch

Speech Enhancement Generative Adversarial Network in PyTorch

segloss icon segloss

A collection of loss functions for medical image segmentation

segmentation icon segmentation

TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmenter icon segmenter

Universal segmenter based on the Universal Dependency framework, written by Y. Shao, Uppsala University

seism icon seism

Supervised Evaluation of Image Segmentation Methods

selectgoodface icon selectgoodface

a program for selecting attractive/serious portraits from a personal photo collection

selectiongan icon selectiongan

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

self-attention-tf icon self-attention-tf

Tensorflow Implementation of "A Structured Self-attentive Sentence Embedding" (ICLR 2017)

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