Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)

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TTP
Overview

On Generating Transferable Targeted Perturbations (ICCV'21)

Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli

Paper: https://arxiv.org/abs/2103.14641

Abstract: While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (ours). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image `distribution' with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains. Based on the proposed objective, we train a generator function that can adaptively synthesize perturbations specific to a given input. Our generative approach is independent of the source or target domain labels, while consistently performs well against state-of-the-art methods on a wide range of attack settings. As an example, we achieve 32.63% target transferability from (an adversarially weak) VGG19BN to (a strong) WideResNet on ImageNet val. set, which is 4x higher than the previous best generative attack and 16x better than instance-specific iterative attack.

Updates & News

  • TTP Training is available (13/07/2021).
  • TTP Evaluation against state-of-the-art input processing defense, NRP, is available (13/07/2021).
  • TTP Evaluation against unknown (black-box) training: SIN, Augmix is available (13/07/2021).

Citation

If you find our work, this repository and pretrained adversarial generators useful. Please consider giving a star and cite our work.

    @InProceedings{naseer2021generating,
        title={On Generating Transferable Targeted Perturbations},
        author={Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli},
        year={2021},
        booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}
    }

Contents

  1. Contributions
  2. Target Transferability Vs Model Disparity
  3. Pretrained Targeted Generator
  4. Training
  5. Evaluation
  6. Why Augmentations boost Transferability?
  7. Why Ensemble of weak Models maximizes Transferability?
  8. Generative Vs Iterative Attacks
  9. Tracking SOTA Targeted Transferability
  10. What Can You Do?
  11. Visual Examples

Contributions

  1. We designed a new training mechanism that allows an adversarial generator to explore augmented adversarial space during training which enhances transferability of adversarial examples during inference.
  2. We propose maximizing the mutual agreement between the given source and the target distributions. Our relaxed objective provides two crucial benifts: a) Generator can now model target ditribution by pushing global statistics between source and target domain closer in the discriminator's latent space, and b) Training is not dependent on class impressions anymore, so our method can provide targeted guidance to the generator without the need of classification boundary information. This allows an attacker to learn targeted generative perturbations from the unsupervised features.
  3. We propose a diverse and consistent experimental settings to evaluate target transferability of adversarial attacks: Unknown Target Model, Unknown Training Mechanism , and Unknown Input Processing.
  4. We provide a platform to track targeted transferability. Please see Tracking SOTA Targeted Transferability. (kindly let us know if you have a new attack method, we will add your results here)

Target Transferability Vs Model Disparity

(top) Our analysis indicates that there is a fundemental difference between Targeted and Untargeted transferability. Model disparity plays a critical role in how the targeted perturbations are transferred from one model to another. Here is an example (average transferability accross 10 targets):

  • Observe that transferring targeted perturbations from a smaller model to a larger one (e.g., ResNet18 to ResNet152 is difficult as we increase the size discrepancy. This phenomenon holds true for untarget transferability.
  • Targeted transferability trend remains the same even from larger to smaller models. For example, target transferability from ResNet152 to ResNet50 is higher than from ResNet152 to ResNet18 even though ResNet18 is weaker than ResNet50. This is where targeted transferability differs from the untargeted case.
  • It is important to note that this behavior is common accross all targeted attacks (iterative or generative) which indicates that this property stems from disparity between the source and the target model. For example, depth between different ResNet models or skip connections between ResNet and DenseNet family reduce the targeted transferability.
  • We note that the dependence on disparity in model architectures can be mitigated with ensemble learning from the models of same family. Targeted transferability from ensemble of e.g., VGG models can be higher than any of the individual VGG model. This is important because an attacker can learn strong transferable targeted patterns from weak models.

Pretrained Targeted Generator

(top) If you find our pretrained Adversarial Generators useful, please consider citing our work.

Class to Label Mapping

Class Number: Class Name
24: Great Grey Owl
99: Goose
245: French Bulldog
344: Hippopotamus
471: Cannon
555: Fire Engine
661: Model T
701: Parachute
802: Snowmobile
919: Street Sign       

Targeted Adversarial Generators trained against Single ImageNet Model.

This is how the pretrianed generators are saved: "netG_Discriminator_sourceDomain_epoch_targetDomain.pth" e.g., netG_vgg11_IN_19_24.pth means that generator is trained agisnt vgg11 (Discriminator) for 20 epoch by maximizing agreement between the source domain (natural images from ImageNet (IN)) and the target domain (images of Grey Owl).

Source Model 24 99 245 344 471 555 661 701 802 919
VGG11 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG13 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG16 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG19 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG11_BN Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG13_BN Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG16_BN Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
VGG19_BN Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
ResNet18 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
ResNet50 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
ResNet101 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
ResNet152 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Dense121 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Dense161 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Dense169 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Dense201 Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign

Targeted Adversarial Generators trained against Ensemble of ImageNet Model.

Source Ensemble 24 99 245 344 471 555 661 701 802 919
VGG{11,13,16,19}BN Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Res{18,50,101,152} Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign
Dense{121,161,169,201} Grey Owl Goose French Bulldog Hippopotamus Cannon Fire Engine Model T Parachute Snowmobile Street Sign

Targeted Adversarial Generators trained against ResNet50.

We trained generator for 100 targets but for ResNet50 only. These generators are for rest of the 90 targets distributed across ImageNet Classes.

Source Model 3 16 36 48 52 69 71 85 107 114 130 138 142 151 162 178 189 193 207 212 228 240 260 261 276 285 291 309 317 328 340 358 366 374 390 393 404 420 430 438 442 453 464 485 491 506 513 523 538 546 569 580 582 599 605 611 629 638 646 652 678 689 707 717 724 735 748 756 766 779 786 791 813 827 836 849 859 866 879 885 893 901 929 932 946 958 963 980 984 992
ResNet50 Tiger Shark Bulbul Terrapin Komodo Dragon Thunder Snake Trilobite Scorpion Quail Jellyfish Slug Flamingo Bustard Dowitcher Chihuahua Beagle Weimaraner Lakeland Terrier Australian Terrier Golden Retriever English Setter Komondor Appenzeller Chow Keeshond Hyena Egyptian Cat Lion Bee Leafhopper Sea Urchin Zebra Polecat Gorilla Langur Eel Anemone Fish Airliner Banjo Basketball Beaker Bell Cote Bookcase Buckle CD Player Chain Saw Coil Cornet Crutch Dome Electric Guitar Garbage Truck Greenhouse Grocery Store Honeycomb iPod Jigsaw Puzzle Lipstick Maillot Maze Military Uniform Neck Brace Overskirt Pay-phone Pickup Pirate Poncho Purse Rain Barrel Rotisserie School Bus Sewing Machine Shopping Cart Spatula Stove Sunglass Teapot Toaster Tractor Umbrella Velvet Wallet Whiskey Jug Ice Lolly Pretzel Cardoon Hay Pizza Volcano Rapeseed Agaric

Training

  1. Source Domain dataset: You can start with paintings dataset such as described in Cross Domain Attack.
  2. Target Domain dataset: We obtain samples of a certain target domain (e.g. ImageNet class) from ImageNet training set.

Run the script with your target of choice:

 ./scripts/train.sh

Evaluation

  1. Download any or all of the pretrained generators to directory "pretrained_generators".
  2. Download ImageNet models trained with stylized ImageNet and augmentations to directory "pretrained_models"

Run the following command to evaluate transferability of a target to (black-box) model on the ImageNet-Val.

  python eval.py  --data_dir data/IN/val --source_model res50 --source_domain IN --target 24 --eps 16 --target_model vgg19_bn 

10/100-Targets (all-source)

Perturb all samples of ImageNet validation (excluding the target class samples) to each of the 10/100 targets and observe the average target transferability to (black-box) model.

  python eval_all.py  --data_dir data/IN/val --source_model res50 --source_domain IN  --eps 16 --num_targets 100 --target_model vgg19_bn 

10-Targets (sub-source)

Select the samples of 10 target classes from ImageNet validation. Perturb the samples of these classes (excluding the target class samples) to each of 10 targets and observe the average target transferability to (black-box) model.

  python eval_sub.py  --data_dir data/IN/val --source_model res50 --source_domain IN --eps 16--target_model vgg19_bn 

Why Augmentations Boost Transferability?

(top) Ilyas et al. showed that adversarial examples can be explained by features of the attacked class label. In our targeted attack case, we wish to imprint the features of the target class distribution onto the source samples within an allowed distance. However, black-box (unknown) model might apply different set of transformations (from one layer to another) to process such features and reduce the target transferability. Training on adversarial augmented samples allows the generator to capture such targeted features that are robust to transformations that may vary from one model to another.

Why Ensemble of Weak Models Maximizes Transferability?

(top) Different models of the same family of networks can exploit different information to make prediction. One such example is shown in here. Generators are trained against Dense121 and Dense169 to target Snowmobile distribution. Unrestricted generator outputs reveal that Dense121 is more focused on Snowmobile's blades while Dense169 emphasizes the background pine tree patterns to discriminate Snowmobile samples. This complementary information from different models of the same family helps the generator to capture more generic global patterns which transfer better than any of the individual models.

Original Image Source Model: Dense121, Target: Snowmobile Source Model: Dense169, Target: Snowmobile

Generative Vs Iterative Attacks

  • Image-specific (iterative) attacks run iterative optimization for each given sample. This optimization is expensive as it has to be repeated for each sample independently. On the other hand, a generator requires training but can adapt to input sample with a farward pass only.
  • Targeted global perturbations are more transferable as indicated by our results. Iteratively optimizing for a target using a single image inherently lacks the ability to model global perturbations. This is where generative methods excel as they can model such perturbations during training phase.

Key Developments made by Iterative Attacks

  • PGD attack has lower transferability due to overfitting (ICRL-2018).
  • MI intorduced momentum. It accumulates gradients over iterations to reduce overfitting (CVPR-2018).
  • DIM introduced input transformations like padding or rescaling to diversify patterns. Think of it as a regulation in the input space to reduce overfitting (CVPR-2019).
  • Po-TRIP introduced triplet loss to push adversarial examples towards the target label while increasing their distance from the original label (CVPR 2020).
  • FDA-fd introduced a method to model class-wise distribution within feature space across differnt layers of a network. Then transfer targeted perturbations from the single optimal layer (ICLR 2020).
  • FDA-N adapts the FDA-fd across multiple layers and the classifier as well (NeurIPS 2020).
  • SGM found that while back-propagating, giving more weightage to gradients from skip connections increases transferability (ICLR 2020).
  • LinBP found that linear back-propagation can boost transferability (NeurIPS 2020).

Key Developments made by Generative Attacks

  • GAP introduced a mechanism to train generative networks against pretrained-models via cross-entropy (CVPR 2018).
  • CDA introduced a mechanism to train generative network against pretrained-model via relativistic cross-entropy (NeurIPS 2019).
  • TTP introudced generative training to match a source and target domain within latent space of a pretrained-model based on gloabl distribution matching objectives. It does not rely on data annotations (labels) or classification boundary information (ICCV 2021).

Tracking SOTA Targeted Transferability

(top) Results on 10-Targets (sub-source) settings.

  • Select 500 samples belonging to 10 targets {24,99,245,344,471,555,661,701,802,919} from ImageNet validation set.
  • Remove the samples of the target class. You are left with 450 samples.
  • Run target attack to map these 450 samples to selected target (perturbation budget l_inf=16).
  • Repeat this process for all the 10 targets.
  • Report average target accuracy.
Updating....Meanwhile, please have a look at our paper. 

Unknown Target Model

(top) Attacker has access to a pretrained discriminator trained on labeled data but has no knowledge about the architecture of the target model.

Method Attack type Source Model Target Model Distance 24 99 245 344 471 555 661 701 802 919 Average
PGD Iterative ResNet50 Dense121 16
MI Iterative ResNet50 Dense121 16
DIM Iterative ResNet50 Dense121 16
Po-TRIP Iterative ResNet50 Dense121 16
FDA-fd Iterative ResNet50 Dense121 16
FDA-N Iterative ResNet50 Dense121 16
SGM Iterative ResNet50 Dense121 16
SGM+LinBP Iterative ResNet50 Dense121 16
GAP Generative ResNet50 Dense121 16
CDA Generative ResNet50 Dense121 16
TTP Generative ResNet50 Dense121 16
PGD Iterative ResNet50 Dense121 16
MI Iterative ResNet50 VGG19_BN 16
DIM Iterative ResNet50 VGG19_BN 16
SGM Iterative ResNet50 VGG19_BN 16
SGM+LinBP Iterative ResNet50 VGG19_BN 16
GAP Generative ResNet50 VGG19_BN 16
CDA Generative ResNet50 VGG19_BN 16
TTP Generative ResNet50 VGG19_BN 16

Unknown Training Mechanism

(top) Attacker has knowledge about the architecture of the target model but unaware of its training mechanism.

Method Attack type Source Model Target Model Distance 24 99 245 344 471 555 661 701 802 919 Average
PGD Iterative ResNet50 SIN 16
MI Iterative ResNet50 SIN 16
DIM Iterative ResNet50 SIN 16
Po-TRIP Iterative ResNet50 SIN 16
FDA-fd Iterative ResNet50 SIN 16
FDA-N Iterative ResNet50 SIN 16
SGM Iterative ResNet50 SIN 16
SGM+LinBP Iterative ResNet50 SIN 16
GAP Generative ResNet50 SIN 16
CDA Generative ResNet50 SIN 16
TTP Generative ResNet50 SIN 16
MI Iterative ResNet50 Augmix 16
DIM Iterative ResNet50 Augmix 16
Po-TRIP Iterative ResNet50 Augmix 16
FDA-fd Iterative ResNet50 Augmix 16
FDA-N Iterative ResNet50 Augmix 16
SGM Iterative ResNet50 Augmix 16
SGM+LinBP Iterative ResNet50 Augmix 16
GAP Generative ResNet50 Augmix 16
CDA Generative ResNet50 Augmix 16
TTP Generative ResNet50 Augmix 16
PGD Iterative ResNet50 ADV 16
MI Iterative ResNet50 ADV 16
DIM Iterative ResNet50 ADV 16
Po-TRIP Iterative ResNet50 ADV 16
FDA-fd Iterative ResNet50 ADV 16
FDA-N Iterative ResNet50 ADV 16
SGM Iterative ResNet50 ADV 16
SGM+LinBP Iterative ResNet50 ADV 16
GAP Generative ResNet50 ADV 16
CDA Generative ResNet50 ADV 16
TTP Generative ResNet50 ADV 16

Unknown Input Processing

(top) Attacker has knowledge about the architecture of the target model but unaware of the input processing defense.

Method Attack type Source Model Input Processing Distance 24 99 245 344 471 555 661 701 802 919 Average
PGD Iterative ResNet50 NRP 16
MI Iterative ResNet50 NRP 16
DIM Iterative ResNet50 NRP 16
Po-TRIP Iterative ResNet50 NRP 16
FDA-fd Iterative ResNet50 NRP 16
FDA-N Iterative ResNet50 NRP 16
SGM Iterative ResNet50 NRP 16
SGM+LinBP Iterative ResNet50 NRP 16
GAP Generative ResNet50 NRP 16
CDA Generative ResNet50 NRP 16
TTP Generative ResNet50 NRP 16

What Can You Do?

We will highlight future research directions here.

References

(top) Code depends on BasicSR. We thank them for their wonderful code base.

Visual Examples

(top) Here are some of the unrestricted targeted patterns found by our method (TTP). This is just for visualization purposes. It is important to note that during inference, these adversaries are projected within a valid distance (e.g l_inf<=16).

Source Model: ResNet50,         Target: Jellyfish
Source Model: ResNet50,         Target: Lipstick
Source Model: ResNet50,         Target: Stove
Source Model: ResNet50,         Target: Rapeseed
Source Model: ResNet50,         Target: Anemone Fish
Source Model: ResNet50,         Target: Banjo
Source Model: ResNet50,         Target: Sea Urchin
Source Model: ResNet50,         Target: Parachute
Source Model: ResNet50,         Target: Buckle
Source Model: ResNet50,         Target: iPOD
Source Model: ResNet50,         Target: Bookcase
Source Model: ResNet50,         Target: Sewing Machine
Issues
  • Our recent work on transferable targeted perturbations

    Our recent work on transferable targeted perturbations

    Hi Muzammal,

    Firstly congratulations on the acceptance of TTP to ICCV 2021. Here I am pleased to bring into view our recent work on transferable targeted perturbations (https://arxiv.org/abs/2012.11207), where we provided a very simple baseline iterative attack that can achieve SOTA performance. We also compared this simple baseline to TTP in the “10-Targets (all-source)” setting and found that it can achieve slightly better results.

    I will appreciate it if you could add this work to your list.

    Best, Zhengyu

    opened by ZhengyuZhao 3
Owner
Muzammal Naseer
PhD student at Australian National University.
Muzammal Naseer
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