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If you are not a researcher, but you are willing, contact me. Email me: yxt.stoaml@gmail.com 6 | 7 | This repository provides state-of-the-art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately. 8 | 9 | You can also submit this [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSe_fFZVCeCVRGGgOQIpoQSXY7mZWynsx7g6WxZEVpO5vJioUA/viewform?embedded=true) if you are new to Github. 10 | 11 | This is an attempt to make one stop for all types of machine learning problems state of the art result. I can not do this alone. I need help from everyone. Please submit the Google form/raise an issue if you find SOTA result for a dataset. Please share this on Twitter, Facebook, and other social media. 12 | 13 | 14 | This summary is categorized into: 15 | 16 | - [Supervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#supervised-learning) 17 | - [Speech](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#speech) 18 | - [Computer Vision](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#computer-vision) 19 | - [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#nlp) 20 | - [Semi-supervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#semi-supervised-learning) 21 | - Computer Vision 22 | - [Unsupervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#unsupervised-learning) 23 | - Speech 24 | - Computer Vision 25 | - [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems/blob/master/README.md#nlp-1) 26 | - [Transfer Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#transfer-learning) 27 | - [Reinforcement Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#reinforcement-learning) 28 | 29 | ## Supervised Learning 30 | 31 | 32 | ### NLP 33 | #### 1. Language Modelling 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 |
Research PaperDatasetsMetricSource CodeYear
Language Models are Unsupervised Multitask Learners
  • PTB
  • WikiText-2
  • Perplexity: 35.76
  • Perplexity: 18.34
Tensorflow 2019
BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL
  • PTB
  • WikiText-2
  • Perplexity: 47.69
  • Perplexity: 40.68
Pytorch 2017
DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS
  • PTB
  • WikiText-2
  • Perplexity: 51.1
  • Perplexity: 44.3
Pytorch 2017
Averaged Stochastic Gradient Descent
with Weight Dropped LSTM or QRNN
  • PTB
  • WikiText-2
  • Perplexity: 52.8
  • Perplexity: 52.0
Pytorch 2017
FRATERNAL DROPOUT
  • PTB
  • WikiText-2
  • Perplexity: 56.8
  • Perplexity: 64.1
Pytorch 2017
Factorization tricks for LSTM networks One Billion Word Benchmark Perplexity: 23.36Tensorflow 2017
88 | 89 | 90 | 91 | 92 | #### 2. Machine Translation 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 138 | 139 | 140 | 141 |
Research PaperDatasetsMetricSource CodeYear
Understanding Back-Translation at Scale
  • WMT 2014 English-to-French
  • WMT 2014 English-to-German
  • BLEU: 45.6
  • BLEU: 35.0
2018
WEIGHTED TRANSFORMER NETWORK FOR 112 | MACHINE TRANSLATION
  • WMT 2014 English-to-French
  • WMT 2014 English-to-German
  • BLEU: 41.4
  • BLEU: 28.9
2017
Attention Is All You Need
  • WMT 2014 English-to-French
  • WMT 2014 English-to-German
  • BLEU: 41.0
  • BLEU: 28.4
2017
NON-AUTOREGRESSIVE 127 | NEURAL MACHINE TRANSLATION
  • WMT16 Ro→En
  • BLEU: 31.44
2017
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
  • NIST02
  • NIST03
  • NIST04
  • NIST05
  • 38.74
  • 36.01
  • 37.54
  • 33.76
    • 137 |     		2017
    142 | 143 | #### 3. Text Classification 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 |
    Research PaperDatasetsMetricSource CodeYear
    Learning Structured Text Representations YelpAccuracy: 68.6 2017
    Attentive ConvolutionYelpAccuracy: 67.36 2017
    170 | 171 | #### 4. Natural Language Inference 172 | Leader board: 173 | 174 | [Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) 175 | 176 | [MultiNLI](https://www.kaggle.com/c/multinli-matched-open-evaluation/leaderboard) 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 |
    Research PaperDatasetsMetricSource CodeYear
    NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE Stanford Natural Language Inference (SNLI)Accuracy: 88.9Tensorflow 2017
    BERT-LARGE (ensemble) Multi-Genre Natural Language Inference (MNLI)
    • Matched accuracy: 86.7
    • Mismatched accuracy: 85.9
    		2018
    203 | 204 | 205 | #### 5. Question Answering 206 | Leader Board 207 | 208 | [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 |
    Research PaperDatasetsMetricSource CodeYear
    BERT-LARGE (ensemble) The Stanford Question Answering Dataset
    • Exact Match: 87.4
    • F1: 93.2
    		2018
    227 | 228 | 229 | #### 6. Named entity recognition 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 |
    Research PaperDatasetsMetricSource CodeYear
    Named Entity Recognition in Twitter using Images and Text Ritter
    • F-measure: 0.59
    		NOT FOUND 2017
    248 | 249 | #### 7. Abstractive Summarization 250 | 251 | Research Paper | Datasets | Metric | Source Code | Year 252 | ------------ | ------------- | ------------ | ------------- | ------------- 253 | [Cutting-off redundant repeating generations
    for neural abstractive summarization](https://aclanthology.info/pdf/E/E17/E17-2047.pdf) | | | NOT YET AVAILABLE | 2017 254 | [Convolutional Sequence to Sequence](https://arxiv.org/pdf/1705.03122.pdf) | | | [PyTorch](https://github.com/facebookresearch/fairseq-py) | 2017 255 | 256 | 257 | #### 8. Dependency Parsing 258 | 259 | Research Paper | Datasets | Metric | Source Code | Year 260 | ------------ | ------------- | ------------ | ------------- | ------------- 261 | [Globally Normalized Transition-Based Neural Networks](https://arxiv.org/pdf/1603.06042.pdf) | | | | 262 | 263 | 264 | ### Computer Vision 265 | 266 | #### 1. Classification 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 |       320 | 321 | 322 | 323 | 324 | 325 | 326 |       329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 |
    Research PaperDatasetsMetricSource CodeYear
    Dynamic Routing Between Capsules
    • MNIST
    • Test Error: 0.25±0.005
    		2017
    High-Performance Neural Networks for Visual Object Classification
    • NORB
    • Test Error: 2.53 ± 0.40
    		2011
    Giant AmoebaNet with GPipe
    • CIFAR-10
    • CIFAR-100
    • ImageNet-1k
    • ...
    • Test Error: 1.0%
    • Test Error: 8.7%
    • Top-1 Error 15.7
    • ...
    		 2018
    ShakeDrop regularization
    • CIFAR-10
    • CIFAR-100
    • Test Error: 2.31%
    • Test Error: 12.19%
    		2017
    Aggregated Residual Transformations for Deep Neural Networks
    • CIFAR-10
    • Test Error: 3.58%
    		2017
    Random Erasing Data Augmentation
    • CIFAR-10
    • CIFAR-100
    • Fashion-MNIST
    • Test Error: 3.08%
      • 317 |
    • Test Error: 17.73%
      • 318 |
    • Test Error: 3.65%
      • 319 |
    		Pytorch 2017
    EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
    • CIFAR-10
    • CIFAR-100
    • Test Error: 3.56%
      • 327 |
    • Test Error: 16.53%
      • 328 |
    		Pytorch 2017
    Dynamic Routing Between Capsules
    • MultiMNIST
    • Test Error: 5%
    		2017
    Learning Transferable Architectures for Scalable Image Recognition
    • ImageNet-1k
    • Top-1 Error:17.3
    		2017
    Squeeze-and-Excitation Networks
    • ImageNet-1k
    • Top-1 Error: 18.68
    		2017
    Aggregated Residual Transformations for Deep Neural Networks
    • ImageNet-1k
    • Top-1 Error: 20.4%
    		2016
    362 | 363 | #### 2. Instance Segmentation 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 |
    Research PaperDatasetsMetricSource CodeYear
    Mask R-CNN
    • COCO
    • Average Precision: 37.1%
    		2017
    382 | 383 | #### 3. Visual Question Answering 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 |
    Research PaperDatasetsMetricSource CodeYear
    Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
    • VQA
    • Overall score: 69
    		2017
    402 | 403 | #### 4. Person Re-identification 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 |       420 | 421 | 422 | 423 | 424 |
    Research PaperDatasetsMetricSource CodeYear
    Random Erasing Data Augmentation
    • Rank-1: 89.13 mAP: 83.93
      • 417 |
    • Rank-1: 84.02 mAP: 78.28
      • 418 |
    • labeled (Rank-1: 63.93 mAP: 65.05) detected (Rank-1: 64.43 mAP: 64.75)
      • 419 |
    		Pytorch 2017
    425 | 426 | ### Speech 427 | [Speech SOTA](https://github.com/syhw/wer_are_we) 428 | #### 1. ASR 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 |
    Research PaperDatasetsMetricSource CodeYear
    The Microsoft 2017 Conversational Speech Recognition System
    • Switchboard Hub5'00
    • WER: 5.1
    		2017
    The CAPIO 2017 Conversational Speech Recognition System
    • Switchboard Hub5'00
    • WER: 5.0
    		2017
    455 | 456 | 457 | ## Semi-supervised Learning 458 | #### Computer Vision 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 492 | 494 | 495 |       500 | 501 | 502 | 503 | 504 | 505 |
    Research PaperDatasetsMetricSource CodeYear
    DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING
    • SVHN
    • NORB
    • Test error: 24.63
    • Test error: 9.88
    		Theano2016
    Virtual Adversarial Training: 477 | a Regularization Method for Supervised and 478 | Semi-supervised Learning
    • MNIST
    • Test error: 1.27
    		2017
    Few Shot Object Detection
    • VOC2007
    • VOC2012
    • mAP : 41.7
    • mAP : 35.4
    		2017
    Unlabeled Samples Generated by GAN 493 | Improve the Person Re-identification Baseline in vitro
    • Rank-1: 83.97 mAP: 66.07
      • 496 |
    • Rank-1: 84.6 mAP: 87.4
      • 497 |
    • Rank-1: 67.68 mAP: 47.13
      • 498 |          
    • Test Accuracy: 84.4
      • 499 |
    		Matconvnet 2017
    506 | 507 | ## Unsupervised Learning 508 | 509 | #### Computer Vision 510 | ##### 1. Generative Model 511 | 512 | 513 | 514 | 515 | 516 | 517 | 518 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 |
    Research PaperDatasetsMetricSource CodeYear
    PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION Unsupervised CIFAR 10Inception score: 8.80 Theano2017
    529 | 530 | ### NLP 531 | 532 | #### Machine Translation 533 | 534 | 535 | 536 | 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 |
    Research PaperDatasetsMetricSource CodeYear
    UNSUPERVISED MACHINE TRANSLATION 545 | USING MONOLINGUAL CORPORA ONLY
    • Multi30k-Task1(en-fr fr-en de-en en-de)
    • BLEU:(32.76 32.07 26.26 22.74)
    		2017
    Unsupervised Neural Machine Translation with Weight Sharing
    • WMT14(en-fr fr-en)
    • WMT16 (de-en en-de)
    • BLEU:(16.97 15.58)
    • BLEU:(14.62 10.86)
    		2018
    562 | 563 | ## Transfer Learning 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 578 | 579 | 580 | 581 | 582 | 583 |
    Research PaperDatasetsMetricSource CodeYear
    One Model To Learn Them All
    • WMT EN → DE
    • WMT EN → FR (BLEU)
    • ImageNet (top-5 accuracy)
    • BLEU: 21.2
    • BLEU:30.5
    • 86%
    		2017
    584 | 585 | 586 | 587 | ## Reinforcement Learning 588 | 589 | 590 | 591 | 592 | 593 | 594 | 595 | 596 | 597 | 598 | 599 | 600 | 601 | 602 | 603 | 604 | 605 | 606 |
    Research PaperDatasetsMetricSource CodeYear
    Mastering the game of Go without human knowledge the game of Go ElO Rating: 51852017
    607 | 608 | Email: yxt.stoaml@gmail.com 609 | --------------------------------------------------------------------------------