PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Overview

Daft-Exprt - PyTorch Implementation

PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

The validation logs up to 70K of synthesized mel and alignment are shown below (VCTK_val_p237-088).

Quickstart

DATASET refers to the names of datasets such as VCTK in the following documents.

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Also, Dockerfile is provided for Docker users.

Inference

You have to download the pretrained models and put them in output/ckpt/DATASET/.

For a multi-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset DATASET --ref_audio REF_AUDIO

to synthesize speech with the style of input audio at REF_AUDIO. The dictionary of learned speakers can be found at preprocessed_data/VCTK/speakers.json, and the generated utterances will be put in output/result/.

Batch Inference

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/DATASET/val.txt --restore_step RESTORE_STEP --mode batch --dataset DATASET

to synthesize all utterances consuming themselves as a reference audio in preprocessed_data/DATASET/val.txt.

Controllability

The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset DATASET --ref_audio REF_AUDIO --duration_control 0.8 --energy_control 0.8

Training

Datasets

The supported datasets are

  • VCTK: The CSTR VCTK Corpus includes speech data uttered by 110 English speakers (multi-speaker TTS) with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
  • Any of multi-speaker TTS dataset (e.g., LibriTTS) can be added following VCTK.

Preprocessing

  • For a multi-speaker TTS with external speaker embedder, download ResCNN Softmax+Triplet pretrained model of philipperemy's DeepSpeaker for the speaker embedding and locate it in ./deepspeaker/pretrained_models/.

  • Run

    python3 prepare_align.py --dataset DATASET
    

    for some preparations.

    For the forced alignment, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences. Pre-extracted alignments for the datasets are provided here. You have to unzip the files in preprocessed_data/DATASET/TextGrid/. Alternately, you can run the aligner by yourself.

    After that, run the preprocessing script by

    python3 preprocess.py --dataset DATASET
    

Training

Train your model with

python3 train.py --dataset DATASET

TensorBoard

Use

tensorboard --logdir output/log

to serve TensorBoard on your localhost. The loss curves, synthesized mel-spectrograms, and audios are shown.

Implementation Issues

  • RangeParameterPredictor is built with BiLSTM rather than a single linear layer with softplus() activation (it is however implemented and named as 'range_param_predictor_paper' in GaussianUpsampling).
  • Use 32 batch size instead of 48 due to memory issues.
  • Use log duration instead of normal duration.
  • Follow FastSpeech2 for the preprocess of pitch and energy.
  • Two options for embedding for the multi-speaker TTS setting: training speaker embedder from scratch or using a pre-trained philipperemy's DeepSpeaker model (as STYLER did). You can toggle it by setting the config (between 'none' and 'DeepSpeaker').
  • DeepSpeaker on VCTK dataset shows clear identification among speakers. The following figure shows the T-SNE plot of extracted speaker embedding.

  • For vocoder, HiFi-GAN and MelGAN are supported.

Citation

@misc{lee2021daft_exprt,
  author = {Lee, Keon},
  title = {Daft-Exprt},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/Daft-Exprt}}
}

References

Comments
  • Is the pretrained model correct?

    Is the pretrained model correct?

    I've downloaded the file @ https://drive.google.com/drive/folders/1rmeW24lrCg_qwPkVI0D4bRNq4HSv_4rE but can't manage to get the model to load, on either the master branch or the v1.0.0 zip file.

    I get the following error on v1.0.0

    Unexpected key(s) in state_dict: "speaker_emb.bias".
            size mismatch for speaker_emb.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([1, 128]).
    

    or master:

    RuntimeError: Error(s) in loading state_dict for DaftExprt:
            size mismatch for speaker_emb.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([5, 128]).
    

    (both with speaker embedding set to "none", with DeepSpeaker I get a different error during preprocessing). Both errors suggest the model is expecting a speaker embedding of size 128, but it's loading an embedding of size 512 (ignoring the first dim n_speakers, I can fix that if needed).

    opened by nmfisher 5
  • FileNotFoundError: [Errno 2] No such file or directory: './preprocessed_data/VCTK/spker_embed/p317-spker_embed.npy'

    FileNotFoundError: [Errno 2] No such file or directory: './preprocessed_data/VCTK/spker_embed/p317-spker_embed.npy'

    Method to Reproduce Error: python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 900000 --mode single --dataset VCTK --ref_audio ../my-voice-analysis/record.wav --speaker_id p317

    Error: FileNotFoundError: [Errno 2] No such file or directory: './preprocessed_data/VCTK/spker_embed/p317-spker_embed.npy'

    opened by anish-rajan 2
  • Unseen Speaker Synthesis

    Unseen Speaker Synthesis

    Hello! Would you be able to synthesize speech using speakers not used during the training? ie. load the unseen speaker's embeddings during inference

    I think by changing this particular line in synthesize.py:

    spker_embeds = np.load(os.path.join(
                preprocess_config["path"]["preprocessed_path"],
                "spker_embed",
                "{}-spker_embed.npy".format(args.speaker_id),
            )) if load_spker_embed else None
    

    However, I'm not sure if it is wise to do this and would result to poor quality. Have you tried doing this?

    opened by migi-gon 2
  • Error when inference

    Error when inference

    Hello! Thanks for the amazing work! I'm meeting something wrong when inference:

    FileNotFoundError: [Errno 2] No such file or directory: './preprocessed_data/VCTK/spker_embed/p317-spker_embed.npy'
    

    What am I missing? Thanks!

    opened by godspirit00 2
  • Question about film?

    Question about film?

    https://github.com/keonlee9420/Daft-Exprt/blob/e8e7a646c73e45332004c570df1f2c367698e42a/model/blocks.py#L62

    this should be gammas * x + betas, right?

    according to https://github.com/ethanjperez/film/blob/fe43ddf8a22b339dcca2efa07091ce9d498955cf/vr/models/filmed_net.py#L26

    one more, do u have some style changed samples?

    opened by azraelkuan 2
  • Bump tensorflow from 2.5.0 to 2.5.1

    Bump tensorflow from 2.5.0 to 2.5.1

    Bumps tensorflow from 2.5.0 to 2.5.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse (CVE-2021-37656)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixDiagV* ops (CVE-2021-37657)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixSetDiagV* ops (CVE-2021-37658)
    • Fixes an undefined behavior arising from reference binding to nullptr and heap OOB in binary cwise ops (CVE-2021-37659)
    • Fixes a division by 0 in inplace operations (CVE-2021-37660)
    • Fixes a crash caused by integer conversion to unsigned (CVE-2021-37661)
    • Fixes an undefined behavior arising from reference binding to nullptr in boosted trees (CVE-2021-37662)
    • Fixes a heap OOB in boosted trees (CVE-2021-37664)
    • Fixes vulnerabilities arising from incomplete validation in QuantizeV2 (CVE-2021-37663)
    • Fixes vulnerabilities arising from incomplete validation in MKL requantization (CVE-2021-37665)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToVariant (CVE-2021-37666)
    • Fixes an undefined behavior arising from reference binding to nullptr in unicode encoding (CVE-2021-37667)
    • Fixes an FPE in tf.raw_ops.UnravelIndex (CVE-2021-37668)
    • Fixes a crash in NMS ops caused by integer conversion to unsigned (CVE-2021-37669)
    • Fixes a heap OOB in UpperBound and LowerBound (CVE-2021-37670)
    • Fixes an undefined behavior arising from reference binding to nullptr in map operations (CVE-2021-37671)
    • Fixes a heap OOB in SdcaOptimizerV2 (CVE-2021-37672)
    • Fixes a CHECK-fail in MapStage (CVE-2021-37673)
    • Fixes a vulnerability arising from incomplete validation in MaxPoolGrad (CVE-2021-37674)
    • Fixes an undefined behavior arising from reference binding to nullptr in shape inference (CVE-2021-37676)
    • Fixes a division by 0 in most convolution operators (CVE-2021-37675)
    • Fixes vulnerabilities arising from missing validation in shape inference for Dequantize (CVE-2021-37677)
    • Fixes an arbitrary code execution due to YAML deserialization (CVE-2021-37678)
    • Fixes a heap OOB in nested tf.map_fn with RaggedTensors (CVE-2021-37679)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse

    ... (truncated)

    Commits
    • 8222c1c Merge pull request #51381 from tensorflow/mm-fix-r2.5-build
    • d584260 Disable broken/flaky test
    • f6c6ce3 Merge pull request #51367 from tensorflow-jenkins/version-numbers-2.5.1-17468
    • 3ca7812 Update version numbers to 2.5.1
    • 4fdf683 Merge pull request #51361 from tensorflow/mm-update-relnotes-on-r2.5
    • 05fc01a Put CVE numbers for fixes in parentheses
    • bee1dc4 Update release notes for the new patch release
    • 47beb4c Merge pull request #50597 from kruglov-dmitry/v2.5.0-sync-abseil-cmake-bazel
    • 6f39597 Merge pull request #49383 from ashahab/abin-load-segfault-r2.5
    • 0539b34 Merge pull request #48979 from liufengdb/r2.5-cherrypick
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Single Speaker TTS

    Single Speaker TTS

    How to perform prosody transfer for single speaker? What value should be given in speaker id, Is any change required in the config file. Can an example be given for the same.

    opened by anupama-deo 0
  • RuntimeError: The size of tensor a (65) must match the size of tensor b (10) at non-singleton dimension 1

    RuntimeError: The size of tensor a (65) must match the size of tensor b (10) at non-singleton dimension 1

    hello I want to train the model using python train.py --dataset VCTK command but i faced following error:

    Number of Daft-Exprt Parameters: 20603604
    Removing weight norm...
    Training:   0%|                                                                                                                | 0/900000 [00:00<?, ?it/s
    Traceback (most recent call last):                                                                                                 | 0/674 [00:00<?, ?it/s]
      File "train.py", line 190, in <module>
        main(args, configs)
      File "train.py", line 85, in main
        output = model(*(batch[2:]))
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 161, in forward
        outputs = self.parallel_apply(replicas, inputs, kwargs)
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 171, in parallel_apply
        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
        output.reraise()
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/_utils.py", line 428, in reraise
        raise self.exc_type(msg)
    RuntimeError: Caught RuntimeError in replica 0 on device 0.
    Original Traceback (most recent call last):
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
        output = module(*input, **kwargs)
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/home/prosody_control/Daft-Exprt-main/model/DaftExprt.py", line 98, in forward
        p_control, e_control, d_control, src_masks, ref_mel_lens, ref_max_mel_len, ref_mel_masks, src_lens
      File "/home/prosody_control/daftenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/home/prosody_control/Daft-Exprt-main/model/modules.py", line 539, in forward
        s_input = p_embed + e_embed + d_embed + encoder_outputs
    RuntimeError: The size of tensor a (65) must match the size of tensor b (10) at non-singleton dimension 1
    
    Training:   0%|                                                                                                   | 1/900000 [00:04<1071:57:08,  4.29s/it]
    Epoch 1:   0%|                                                                                                                    | 0/674 [00:04<?, ?it/s]```
    opened by niasalva 0
Releases(v1.0.1)
Owner
Keon Lee
Expressive Speech Synthesis | Conversational AI | Open-domain Dialog | NLP | Generative Models | Empathic Computing | HCI
Keon Lee
Code for intrusion detection system (IDS) development using CNN models and transfer learning

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

Western OC2 Lab 38 Dec 12, 2022
PyTorch reimplementation of the paper Involution: Inverting the Inherence of Convolution for Visual Recognition [CVPR 2021].

Involution: Inverting the Inherence of Convolution for Visual Recognition Unofficial PyTorch reimplementation of the paper Involution: Inverting the I

Christoph Reich 100 Dec 01, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight)

UPDeT Official Implementation of UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers (ICLR 2021 spotlight) The

hhhusiyi 96 Dec 22, 2022
Synthesizing and manipulating 2048x1024 images with conditional GANs

pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio

NVIDIA Corporation 6k Dec 27, 2022
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

63 Oct 17, 2022
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

Kamilė Stankevičiūtė 36 Nov 21, 2022
Tooling for converting STAC metadata to ODC data model

手语识别 0、使用到的模型 (1). openpose,作者:CMU-Perceptual-Computing-Lab https://github.com/CMU-Perceptual-Computing-Lab/openpose (2). 图像分类classification,作者:Bubbl

Open Data Cube 65 Dec 20, 2022
Exploiting Robust Unsupervised Video Person Re-identification

Exploiting Robust Unsupervised Video Person Re-identification Implementation of the proposed uPMnet. For the preprint, please refer to [Arxiv]. Gettin

1 Apr 09, 2022
The repo of Feedback Networks, CVPR17

Feedback Networks http://feedbacknet.stanford.edu/ Paper: Feedback Networks, CVPR 2017. Amir R. Zamir*,Te-Lin Wu*, Lin Sun, William B. Shen, Bertram E

Stanford Vision and Learning Lab 87 Nov 19, 2022
AI that generate music

PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia

Annas 28 Nov 27, 2022
Test-Time Personalization with a Transformer for Human Pose Estimation, NeurIPS 2021

Transforming Self-Supervision in Test Time for Personalizing Human Pose Estimation This is an official implementation of the NeurIPS 2021 paper: Trans

41 Nov 28, 2022
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

Zhuo Zheng 125 Dec 13, 2022
covid question answering datasets and fine tuned models

Covid-QA Fine tuned models for question answering on Covid-19 data. Hosted Inference This model has been contributed to huggingface.Click here to see

Abhijith Neil Abraham 19 Sep 09, 2021
This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation

This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation (Guillaume Couairon, Holger

Meta Research 31 Oct 17, 2022
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination

InsGen - Data-Efficient Instance Generation from Instance Discrimination Data-Efficient Instance Generation from Instance Discrimination Ceyuan Yang,

GenForce: May Generative Force Be with You 93 Dec 25, 2022
MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

MarcoPolo is a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering Overview MarcoPolo

Chanwoo Kim 13 Dec 18, 2022