3,930 research outputs found
Adversarial content manipulation for analyzing and improving model robustness
The recent rapid progress in machine learning systems has opened up many real-world applications --- from recommendation engines on web platforms to safety critical systems like autonomous vehicles. A model deployed in the real-world will often encounter inputs far from its training distribution. For example, a self-driving car might come across a black stop sign in the wild. To ensure safe operation, it is vital to quantify the robustness of machine learning models to such out-of-distribution data before releasing them into the real-world. However, the standard paradigm of benchmarking machine learning models with fixed size test sets drawn from the same distribution as the training data is insufficient to identify these corner cases efficiently. In principle, if we could generate all valid variations of an input and measure the model response, we could quantify and guarantee model robustness locally. Yet, doing this with real world data is not scalable. In this thesis, we propose an alternative, using generative models to create synthetic data variations at scale and test robustness of target models to these variations. We explore methods to generate semantic data variations in a controlled fashion across visual and text modalities. We build generative models capable of performing controlled manipulation of data like changing visual context, editing appearance of an object in images or changing writing style of text. Leveraging these generative models we propose tools to study robustness of computer vision systems to input variations and systematically identify failure modes. In the text domain, we deploy these generative models to improve diversity of image captioning systems and perform writing style manipulation to obfuscate private attributes of the user. Our studies quantifying model robustness explore two kinds of input manipulations, model-agnostic and model-targeted. The model-agnostic manipulations leverage human knowledge to choose the kinds of changes without considering the target model being tested. This includes automatically editing images to remove objects not directly relevant to the task and create variations in visual context. Alternatively, in the model-targeted approach the input variations performed are directly adversarially guided by the target model. For example, we adversarially manipulate the appearance of an object in the image to fool an object detector, guided by the gradients of the detector. Using these methods, we measure and improve the robustness of various computer vision systems -- specifically image classification, segmentation, object detection and visual question answering systems -- to semantic input variations.Der schnelle Fortschritt von Methoden des maschinellen Lernens hat viele neue Anwendungen ermöglicht – von Recommender-Systemen bis hin zu sicherheitskritischen Systemen wie autonomen Fahrzeugen. In der realen Welt werden diese Systeme oft mit Eingaben außerhalb der Verteilung der Trainingsdaten konfrontiert. Zum Beispiel könnte ein autonomes Fahrzeug einem schwarzen Stoppschild begegnen. Um sicheren Betrieb zu gewährleisten, ist es entscheidend, die Robustheit dieser Systeme zu quantifizieren, bevor sie in der Praxis eingesetzt werden. Aktuell werden diese Modelle auf festen Eingaben von derselben Verteilung wie die Trainingsdaten evaluiert. Allerdings ist diese Strategie unzureichend, um solche Ausnahmefälle zu identifizieren. Prinzipiell könnte die Robustheit “lokal” bestimmt werden, indem wir alle zulässigen Variationen einer Eingabe generieren und die Ausgabe des Systems überprüfen. Jedoch skaliert dieser Ansatz schlecht zu echten Daten. In dieser Arbeit benutzen wir generative Modelle, um synthetische Variationen von Eingaben zu erstellen und so die Robustheit eines Modells zu überprüfen. Wir erforschen Methoden, die es uns erlauben, kontrolliert semantische Änderungen an Bild- und Textdaten vorzunehmen. Wir lernen generative Modelle, die kontrollierte Manipulation von Daten ermöglichen, zum Beispiel den visuellen Kontext zu ändern, die Erscheinung eines Objekts zu bearbeiten oder den Schreibstil von Text zu ändern. Basierend auf diesen Modellen entwickeln wir neue Methoden, um die Robustheit von Bilderkennungssystemen bezüglich Variationen in den Eingaben zu untersuchen und Fehlverhalten zu identifizieren. Im Gebiet von Textdaten verwenden wir diese Modelle, um die Diversität von sogenannten Automatische Bildbeschriftung-Modellen zu verbessern und Schreibtstil-Manipulation zu erlauben, um private Attribute des Benutzers zu verschleiern. Um die Robustheit von Modellen zu quantifizieren, werden zwei Arten von Eingabemanipulationen untersucht: Modell-agnostische und Modell-spezifische Manipulationen. Modell-agnostische Manipulationen basieren auf menschlichem Wissen, um bestimmte Änderungen auszuwählen, ohne das entsprechende Modell miteinzubeziehen. Dies beinhaltet das Entfernen von für die Aufgabe irrelevanten Objekten aus Bildern oder Variationen des visuellen Kontextes. In dem alternativen Modell-spezifischen Ansatz werden Änderungen vorgenommen, die für das Modell möglichst ungünstig sind. Zum Beispiel ändern wir die Erscheinung eines Objekts um ein Modell der Objekterkennung täuschen. Dies ist durch den Gradienten des Modells möglich. Mithilfe dieser Werkzeuge können wir die Robustheit von Systemen zur Bildklassifizierung oder -segmentierung, Objekterkennung und Visuelle Fragenbeantwortung quantifizieren und verbessern
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval
Every hour, huge amounts of visual contents are posted on social media and
user-generated content platforms. To find relevant videos by means of a natural
language query, text-video retrieval methods have received increased attention
over the past few years. Data augmentation techniques were introduced to
increase the performance on unseen test examples by creating new training
samples with the application of semantics-preserving techniques, such as color
space or geometric transformations on images. Yet, these techniques are usually
applied on raw data, leading to more resource-demanding solutions and also
requiring the shareability of the raw data, which may not always be true, e.g.
copyright issues with clips from movies or TV series. To address this
shortcoming, we propose a multimodal data augmentation technique which works in
the feature space and creates new videos and captions by mixing semantically
similar samples. We experiment our solution on a large scale public dataset,
EPIC-Kitchens-100, and achieve considerable improvements over a baseline
method, improved state-of-the-art performance, while at the same time
performing multiple ablation studies. We release code and pretrained models on
Github at https://github.com/aranciokov/FSMMDA_VideoRetrieval.Comment: Accepted for presentation at 30th ACM International Conference on
Multimedia (ACM MM
Know your audience: specializing grounded language models with listener subtraction
Effective communication requires adapting to the idiosyncrasies of each
communicative context--such as the common ground shared with each partner.
Humans demonstrate this ability to specialize to their audience in many
contexts, such as the popular game Dixit. We take inspiration from Dixit to
formulate a multi-agent image reference game where a (trained) speaker model is
rewarded for describing a target image such that one (pretrained) listener
model can correctly identify it among distractors, but another listener cannot.
To adapt, the speaker must exploit differences in the knowledge it shares with
the different listeners. We show that finetuning an attention-based adapter
between a CLIP vision encoder and a large language model in this contrastive,
multi-agent setting gives rise to context-dependent natural language
specialization from rewards only, without direct supervision. Through
controlled experiments, we show that training a speaker with two listeners that
perceive differently, using our method, allows the speaker to adapt to the
idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the
specialization to real-world data. Our experiments demonstrate a method for
specializing grounded language models without direct supervision and highlight
the interesting research challenges posed by complex multi-agent communication.Comment: 28 pages, 9 figure
Know your audience: specializing grounded language models with listener subtraction
Effective communication requires adapting
to the idiosyncrasies of each communicative
context—such as the common ground shared
with each partner. Humans demonstrate this
ability to specialize to their audience in many
contexts, such as the popular game Dixit. We
take inspiration from Dixit to formulate a multiagent image reference game where a (trained)
speaker model is rewarded for describing a target image such that one (pretrained) listener
model can correctly identify it among distractors, but another listener cannot. To adapt, the
speaker must exploit differences in the knowledge it shares with the different listeners. We
show that finetuning an attention-based adapter
between a CLIP vision encoder and a large language model in this contrastive, multi-agent
setting gives rise to context-dependent natural language specialization from rewards only,
without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communicatio
Causally-Inspired Generalizable Deep Learning Methods under Distribution Shifts
Deep learning methods achieved remarkable success in various areas of artificial intelligence, due to their powerful distribution-matching capabilities. However, these successes rely heavily on the i.i.d assumption, i.e., the data distributions in the training and test datasets are the same. In this way, current deep learning methods typically exhibit poor generalization under distribution shift, performing poorly on test data with a distribution that differs from the training data. This significantly hinders the application of deep learning methods to real-world scenarios, as the distribution of test data is not always the same as the training distribution in our rapidly evolving world.
This thesis aims to discuss how to construct generalizable deep learning methods under distribution shifts. To achieve this, the thesis first models one prediction task as a structural causal model (SCM) which establishes the relationship between variables using directed acyclic graphs. In an SCM, some variables are easily changed across domains while others are not. However, deep learning methods often unintentionally mix invariant variables with easily changed variables, and thus deviate the learned model from the true one, resulting in the poor generalization ability under distribution shift.
To remedy this issue, we propose specific algorithms to model such an invariant part of the SCM with deep learning methods, and experimentally show it is beneficial for the trained model to generalize well into different distributions of the same task. Last, we further propose to identify and model the variant information in the new test distribution so that we can fully adapt the trained deep learning model accordingly.
We show the method can be extended for several practical applications, such as classification under label shift, image translation under semantics shift, robotics control in dynamics generalization and generalizing large language models into visual question-answer tasks
Controlling Style and Semantics in Weakly-Supervised Image Generation
We propose a weakly-supervised approach for conditional image generation of
complex scenes where a user has fine control over objects appearing in the
scene. We exploit sparse semantic maps to control object shapes and classes, as
well as textual descriptions or attributes to control both local and global
style. In order to condition our model on textual descriptions, we introduce a
semantic attention module whose computational cost is independent of the image
resolution. To further augment the controllability of the scene, we propose a
two-step generation scheme that decomposes background and foreground. The label
maps used to train our model are produced by a large-vocabulary object
detector, which enables access to unlabeled data and provides structured
instance information. In such a setting, we report better FID scores compared
to fully-supervised settings where the model is trained on ground-truth
semantic maps. We also showcase the ability of our model to manipulate a scene
on complex datasets such as COCO and Visual Genome.Comment: European Conference on Computer Vision (ECCV) 2020, Spotlight. Code
at https://github.com/dariopavllo/style-semantic
- …