Search Results for author:
Found 30 papers, 23 papers with code
In Defense of Image Pre-Training for Spatiotemporal Recognition
Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels.
Fast AdvProp
Specifically, our modifications in Fast AdvProp are guided by the hypothesis that disentangled learning with adversarial examples is the key for performance improvements, while other training recipes (e. g., paired clean and adversarial training samples, multi-step adversarial attackers) could be largely simplified.
Learning to Bootstrap for Combating Label Noise
Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.
Ranked #5 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
iBOT: Image BERT Pre-Training with Online Tokenizer
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
Ranked #1 on
Self-Supervised Image Classification
on ImageNet
Are Transformers More Robust Than CNNs?
Transformer emerges as a powerful tool for visual recognition.
Ranked #1 on
Adversarial Robustness
on ImageNet-A
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images.
Image BERT Pre-training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection.
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.
Shape-Texture Debiased Neural Network Training
To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Ranked #340 on
Image Classification
on ImageNet
Smooth Adversarial Training
SAT also works well with larger networks: it helps EfficientNet-L1 to achieve 82. 2% accuracy and 58. 6% robustness on ImageNet, outperforming the previous state-of-the-art defense by 9. 5% for accuracy and 11. 6% for robustness.
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
PatchAttack induces misclassifications by superimposing small textured patches on the input image.
Neural Architecture Search for Lightweight Non-Local Networks
However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks.
Ranked #38 on
Neural Architecture Search
on ImageNet
Adversarial Examples Improve Image Recognition
We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.
Ranked #105 on
Image Classification
on ImageNet
Universal Physical Camouflage Attacks on Object Detectors
In this paper, we study physical adversarial attacks on object detectors in the wild.
Intriguing properties of adversarial training at scale
This two-domain hypothesis may explain the issue of BN when training with a mixture of clean and adversarial images, as estimating normalization statistics of this mixture distribution is challenging.
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations.
Feature Denoising for Improving Adversarial Robustness
This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks.
Learning Transferable Adversarial Examples via Ghost Networks
The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Improving Transferability of Adversarial Examples with Input Diversity
We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.
Single-Shot Object Detection with Enriched Semantics
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
Visual Concepts and Compositional Voting
We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.
Mitigating Adversarial Effects Through Randomization
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years.
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.
Detecting Semantic Parts on Partially Occluded Objects
Our approach detects semantic parts by accumulating the confidence of local visual cues.
Adversarial Examples for Semantic Segmentation and Object Detection
Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e. g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification.
