J0nasW/paperswithcode huggingface dataset:
Task: Data Poisoning
Area: adversarial

**Data Poisoning** is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).


<span class="description-source">Source: [Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics ](https://arxiv.org/abs/1907.07296)</span>
Task: Backdoor Defense for Data-Free Distillation with Poisoned Teachers
Area: adversarial

Defend against backdoor attack from poisoned teachers.
Task: Backdoor Attack
Area: adversarial

Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.
Task: Adversarial Defense
Area: adversarial

Competitions with currently unpublished results:

- [TrojAI](https://pages.nist.gov/trojai/)
Task: Real-World Adversarial Attack
Area: adversarial

Adversarial attacks that are presented in the real world
Task: Adversarial Attack
Area: adversarial

An **Adversarial Attack** is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.


<span class="description-source">Source: [Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks ](https://arxiv.org/abs/2002.05388)</span>
Task: Adversarial Text
Area: adversarial

Adversarial Text refers to a specialised text sequence that is designed specifically to influence the prediction of a language model. Generally, Adversarial Text attack are carried out on Large Language Models (LLMs). Research on understanding different adversarial approaches can help us build effective defense mechanisms to detect malicious text input and build robust language models.
Task: Adversarial Robustness
Area: adversarial

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
Task: Model extraction
Area: adversarial

Model extraction attacks, aka model stealing attacks, are used to extract the parameters from the target model. Ideally, the adversary will be able to steal and replicate a model that will have a very similar performance to the target model.
Task: Audio declipping
Area: audio

Audio declipping is the task of estimating the original audio signal given its clipped measurements.
Task: Voice Conversion
Area: audio

**Voice Conversion** is a technology that modifies the speech of a source speaker and makes their speech sound like that of another target speaker without changing the linguistic information.


<span class="description-source">Source: [Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet ](https://arxiv.org/abs/1903.12389)</span>
Task: Bandwidth Extension
Area: audio

Bandwidth extension is the task of expanding the bandwidth of a signal in a way that approximates the original or desired higher bandwidth signal.
Task: Audio Tagging
Area: audio

Audio tagging is a task to predict the tags of audio clips. Audio tagging tasks include music tagging, acoustic scene classification, audio event classification, etc.
Task: Directional Hearing
Area: audio

Extremely low-latency audio source separation from a known direction of arrival.
Task: Speaker Orientation
Area: audio

Direction of Voice or speaker orientation of the person with respect to the target device.
Task: Audio Generation
Area: audio

Audio generation (synthesis) is the task of generating raw audio such as speech.

<span style="color:grey; opacity: 0.6">( Image credit: [MelNet](https://arxiv.org/pdf/1906.01083v1.pdf) )</span>
Task: Sound Event Detection
Area: audio

**Sound Event Detection** (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.


<span class="description-source">Source: [A report on sound event detection with different binaural features ](https://arxiv.org/abs/1710.02997)</span>
Task: Audio Signal Processing
Area: audio

This is a general task that covers transforming audio inputs into audio outputs, not limited to existing PaperWithCode categories of Source Separation, Denoising, Classification, Recognition, etc.
Task: Direction of Arrival Estimation
Area: audio

Estimating the direction-of-arrival (DOA) of a sound source from multi-channel recordings.
Task: Soundscape evaluation
Area: audio

Evaluation of soundscape in accordance to ISO/TS 12913-2
Task: Room Impulse Response (RIR)
Area: audio

**Room Impulse Response (RIR)** is an audio signal processing task that involves capturing and analyzing the acoustic characteristics of a room or an environment. The goal is to measure and model the way sound waves interact with the space, including reflections, reverberation, and echoes.
Task: Active Speaker Localization
Area: audio

Active Speaker Localization (ASL) is the process of spatially localizing an active speaker (talker) in an environment using either audio, vision or both.
Task: Acoustic Scene Classification
Area: audio

The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded.

Source: [DCASE 2019](http://dcase.community/challenge2019/task-acoustic-scene-classification)
Source: [DCASE 2018](https://dcase.community/challenge2018/task-acoustic-scene-classification)
Task: Sound Event Localization and Detection
Area: audio

Given multichannel audio input, a sound event detection and localization (SELD) system outputs a temporal activation track for each of the target sound classes, along with one or more corresponding spatial trajectories when the track indicates activity. This results in a spatio-temporal characterization of the acoustic scene that can be used in a wide range of machine cognition tasks, such as inference on the type of environment, self-localization, navigation without visual input or with occluded targets, tracking of specific types of sound sources, smart-home applications, scene visualization systems, and audio surveillance, among others.
Task: Language Identification
Area: audio

Language identification is the task of determining the language of a text.
Task: Audio Super-Resolution
Area: audio

AUDIO SUPER-RESOLUTION or speech bandwidth extension (Upsampling Ratio = 2)
Task: Target Speaker Extraction
Area: audio

Extract the dialogue content of the specified target in a multi-person dialogue.
Task: Audio Classification
Area: audio

**Audio Classification** is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
Task: Environmental Sound Classification
Area: audio

Classification of Environmental Sounds. Most often sounds found in Urban environments. Task related to noise monitoring.
Task: Audio Source Separation
Area: audio

**Audio Source Separation** is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals).


<span class="description-source">Source: [Model selection for deep audio source separation via clustering analysis ](https://arxiv.org/abs/1910.12626)</span>
Task: Audio Effects Modeling
Area: audio

Modeling of audio effects such as reverberation, compression, distortion, etc.
Task: Few-Shot Audio Classification
Area: audio

Few-shot classification for audio signals. Presents a unique challenge compared to other few-shot domains as we deal with temporal dependencies as well
Task: Music Generation
Area: audio

**Music Generation** is the task of generating music or music-like sounds from a model or algorithm. The goal is to produce a sequence of notes or sound events that are similar to existing music in some way, such as having the same style, genre, or mood.
Task: Streaming Target Sound Extraction
Area: audio

This task is a variant of the [Target Sound Extraction](https://paperswithcode.com/task/target-sound-extraction) task, with the constraint of causal streaming inference. Aiming for an algorithmic latency of less than 20 ms, at each time step, streaming audio models operate on an input audio chunk of length less than 20 ms. The causal constraint means that the model only has the knowledge of past chunks and no future chunks.
Task: Real-time Directional Hearing
Area: audio

Directional hearing models that also support real-time on-device inference
Task: Voice Anti-spoofing
Area: audio

Discriminate genuine speech and spoofing attacks
Task: Audio Dequantization
Area: audio

Audio Dequantization is a process of estimating the original signal from its quantized counterpart.
Task: Single-Label Target Sound Extraction
Area: audio

Single-Label Target Sound Extraction is the task of extracting a  given class of sounds from an audio mixture. The audio mixture may contain background noise with a relatively low amplitude compared to the foreground mixture components. The choice of the sound class is provided as input to the model in form of a string, integer, or a one-hot encoding of the sound class.
Task: Target Sound Extraction
Area: audio

Target Sound Extraction is the task of extracting a sound corresponding to a given class from an audio mixture. The audio mixture may contain background noise with a relatively low amplitude compared to the foreground mixture components. The choice of the sound class is provided as input to the model in form of a string, integer, or a one-hot encoding of the sound class.
Task: Acoustic Novelty Detection
Area: audio

Detect novel events given acoustic signals, either in domestic or outdoor environments.
Task: NMT
Area: computer-code

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
Task: Code Generation
Area: computer-code

**Code Generation** is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.


<span class="description-source">Source: [Deep Learning for Source Code Modeling and Generation ](https://arxiv.org/abs/2002.05442)</span>

Image source: [Measuring Coding Challenge Competence With APPS](https://paperswithcode.com/paper/measuring-coding-challenge-competence-with)
Task: Source Code Summarization
Area: computer-code

**Code Summarization** is a task that tries to comprehend code and automatically generate descriptions directly from the source code.


<span class="description-source">Source: [Improving Automatic Source Code Summarization via Deep Reinforcement Learning ](https://arxiv.org/abs/1811.07234)</span>
Task: Code Translation
Area: computer-code

Code translation is the process of converting code written in one programming language to another programming language while maintaining the same functionality. This process is also known as code conversion, source-to-source translation, or transpilation. Code translation is often performed when developers want to take advantage of new programming languages, improve code performance, or maintain legacy systems. Some common examples include translating code from Python to Java, or from JavaScript to TypeScript.
Task: Program Repair
Area: computer-code

Task of teaching ML models to modify an existing program to fix a bug in a given code.
Task: Tiling & Deployment
Area: computer-code

Data tiling over 3 memory hierarchy levels and deployment on microcontroller.
Task: Neural Network simulation
Area: computer-code

Simulation of abstract or biophysical neural networks in silico
Task: Edit script generation
Area: computer-code

Generating edit scripts by comparing 2 different files or strings to convert one to another. this script will contain instruction like insert, delete and substitute.
Task: Chart Question Answering
Area: computer-code

Question Answering task on charts images
Task: Code Documentation Generation
Area: computer-code

Code Documentation Generation is a supervised task where a code function is the input to the model, and the model generates the documentation for this function.

Description from: [CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing](https://arxiv.org/pdf/2104.02443.pdf)
Task: EditCompletion
Area: computer-code

Given a code snippet that is partially edited, the goal is to predict a completion of the edit for the rest of the snippet.
Task: File difference
Area: computer-code

Generate edit script comparing 2 strings or files, which contains instruction of insert, delete and substitute to convert first string to the second.
Task: Video Defect Classification
Area: computer-code

Quick-View (QV) Inspection is one commonly-used technology. However, it is quite labor-intensive to find defects from a huge number of QV videos. To tackle this problem, we propose a video defect classification task, which is to predict the categories of pipe
defects in a short QV video.
Task: Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
Area: computer-code

Code for Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
Task: SQL-to-Text
Area: computer-code

<span style="color:grey; opacity: 0.6">( Image credit: [SQL-to-Text Generation with Graph-to-Sequence Model](https://arxiv.org/pdf/1809.05255v2.pdf) )</span>
Task: Program Synthesis
Area: computer-code

Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.

Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.
Task: Log Parsing
Area: computer-code

**Log Parsing** is the task of transforming unstructured log data into a structured format that can be used to train machine learning algorithms. The structured log data is then used to identify patterns, trends, and anomalies, which can support decision-making and improve system performance, security, and reliability. The log parsing process involves the extraction of relevant information from log files, the conversion of this information into a standardized format, and the storage of the structured data in a database or other data repository.
Task: Program induction
Area: computer-code

Generating program code for domain-specific tasks
Task: Text-To-SQL
Area: computer-code

**Text-to-SQL** is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.

<span style="color:grey; opacity: 0.6">( Image credit: [SyntaxSQLNet](https://arxiv.org/pdf/1810.05237v2.pdf) )</span>
Task: Text-to-Code Generation
Area: computer-code

**Text-to-Code Generation** is a task where we can generate code based on the natural language description.

Source: [Text-to-code Generation with TensorFlow, 🤗 & MBPP](https://www.kaggle.com/code/rhtsingh/text-to-code-generation-with-tensorflow-mbpp)
Task: Paraphrase Generation
Area: computer-code

Paraphrase Generation involves transforming a natural language sentence to a new sentence, that has the same semantic meaning but a different syntactic or lexical surface form.
Task: Code Search
Area: computer-code

The goal of **Code Search** is to retrieve code fragments from a large code corpus that most closely match a developer’s intent, which is expressed in natural language.


<span class="description-source">Source: [When Deep Learning Met Code Search ](https://arxiv.org/abs/1905.03813)</span>
Task: Annotated Code Search
Area: computer-code

Annotated code search is the retrieval of code snippets paired with brief descriptions of their intent using natural language queries.
Task: Video Propagation
Area: computer-vision

Propagating information in processed frames to unprocessed frames
Task: Few Shot Temporal Action Localization
Area: computer-vision

Detect Action using few labeled samples
Task: One-Shot Object Detection
Area: computer-vision

<span style="color:grey; opacity: 0.6">( Image credit: [Siamese Mask R-CNN
](https://github.com/bethgelab/siamese-mask-rcnn) )</span>
Task: Single class few-shot image synthesis
Area: computer-vision

The goal of single class few-shot image synthesis task is to learn a generative model that can generate
samples with visual attributes from as few as two or more input images images belonging to the same class.
Task: Sperm Morphology Classification
Area: computer-vision

Multi-class classification of sperm head morphology.
Task: Salt-And-Pepper Noise Removal
Area: computer-vision

Salt-and-pepper noise is a form of noise sometimes seen on images. It is also known as impulse noise. This noise can be caused by sharp and sudden disturbances in the image signal. It presents itself as sparsely occurring white and black pixels.

<span style="color:grey; opacity: 0.6">( Image credit: [NAMF](https://arxiv.org/pdf/1910.07787v1.pdf) )</span>
Task: Style Transfer
Area: computer-vision

**Style Transfer** is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.

<span style="color:grey; opacity: 0.6">( Image credit: [A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf) )</span>
Task: 3D Face Animation
Area: computer-vision

Image: [Cudeiro et al](https://arxiv.org/pdf/1905.03079v1.pdf)
Task: Traffic Sign Recognition
Area: computer-vision

Traffic sign recognition is the task of recognising traffic signs in an image or video.

<span style="color:grey; opacity: 0.6">( Image credit: [Novel Deep Learning Model for Traffic Sign Detection Using Capsule
Networks ](https://arxiv.org/pdf/1805.04424v1.pdf) )</span>
Task: Facial Recognition and Modelling
Area: computer-vision

Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
Task: Unsupervised 3D Semantic Segmentation
Area: computer-vision

Unsupervised 3D Semantic Segmentation
Task: 3D Feature Matching
Area: computer-vision

Image: [Choy et al](https://paperswithcode.com/paper/fully-convolutional-geometric-features)
Task: SVBRDF Estimation
Area: computer-vision

SVBRDF Estimation
Task: Robust Face Alignment
Area: computer-vision

Robust face alignment is the task of face alignment in unconstrained (non-artificial) conditions.

<span style="color:grey; opacity: 0.6">( Image credit: [Deep Alignment Network](https://github.com/MarekKowalski/DeepAlignmentNetwork) )</span>
Task: Offline surgical phase recognition
Area: computer-vision

Offline surgical phase recognition: the first 40 videos to train, the last 40 videos to test.
Task: Sensor Modeling
Area: computer-vision

<span style="color:grey; opacity: 0.6">( Image credit: [LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving](https://arxiv.org/abs/1905.07290) )</span>
Task: Face Quality Assessement
Area: computer-vision

Estimate the usability of a given face image for recognition
Task: Robust Face Recognition
Area: computer-vision

Robust face recognition is the task of performing recognition in an unconstrained environment, where there is variation of view-point, scale, pose, illumination and expression of the face images.

<span style="color:grey; opacity: 0.6">( Image credit: [MeGlass dataset](https://github.com/cleardusk/MeGlass) )</span>
Task: Adversarial Attack Detection
Area: computer-vision

The detection of adversarial attacks.
Task: Satellite Image Classification
Area: computer-vision

Satellite image classification is the most significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which is based on diversity structures of the image that involve rigorous validation of the training samples depending on the used classification algorithm.
Task: Camera Calibration
Area: computer-vision

Camera calibration involves estimating camera parameters(including camera intrinsics and extrinsics) to infer geometric features from captured sequences, which is crucial for computer vision and robotics. Driven by different architectures of the neural network,
the researchers have developed two main paradigms for learning-based camera calibration and its applications. One is Regression-based Calibration，Reconstruction-based Calibration is another.
Task: Referring Image Matting (RefMatte-RW100)
Area: computer-vision

Expression-based referring image matting on natural images and manually labelled annotations, i.e., RefMatte-RW100, taking the image and a flowery expression as the input.
Task: Video Visual Relation Detection
Area: computer-vision

**Video Visual Relation Detection (VidVRD)** aims to detect instances of visual relations of interest in a video, where a visual relation instance is represented by a relation triplet <subject, predicate, object> with the trajectories of the subject and object. As compared to still images, videos provide a more natural set of features for detecting visual relations, such as the dynamic relations like “A-follow-B” and “A-towards-B”, and temporally changing relations like “A-chase-B” followed by “A-hold-B”. Yet, VidVRD is technically more challenging than ImgVRD due to the difficulties in accurate object tracking and diverse relation appearances in the video domain.

<span class="description-source">Source: [ImageNet-VidVRD Video Visual Relation Dataset](https://xdshang.github.io/docs/imagenet-vidvrd.html)</span>
Task: Person Re-Identification
Area: computer-vision

**Person Re-Identification** is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
Task: Mutual Gaze
Area: computer-vision

Detect if two people are looking at each other
Task: Affordance Recognition
Area: computer-vision

Affordance recognition from Human-Object Interaction
Task: Scene Parsing
Area: computer-vision

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. [MIT Description](http://sceneparsing.csail.mit.edu/#:~:text=Scene%20parsing%20is%20to%20segment,the%20algorithms%20of%20scene%20parsing.)
Task: 3D Shape Classification
Area: computer-vision

Image: [Sun et al](https://arxiv.org/pdf/1804.04610v1.pdf)
Task: 3D Character Animation From A Single Photo
Area: computer-vision

Image: [Weng et al](https://arxiv.org/pdf/1812.02246v1.pdf)