Deep learning is about creating computer programs that can learn and improve by themselves, using structures inspired by the human brain. It’s like teaching a virtual brain to recognize and understand things! Deep learning is a subfield of machine learning, which is a broader field in artificial intelligence.
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Imagine you have a computer program that can learn from experience. Instead of being explicitly programmed to perform a task, it learns and improves as it gets more data.
2. What is Deep Learning?
Deep learning is a specific kind of machine learning inspired by the structure and function of the human brain. It involves neural networks, which are layered structures of algorithms that mimic the way the brain works to process information.
3. Neural Networks:
Picture a neural network as a virtual brain made of interconnected nodes (neurons). Each connection has a weight, and the network learns by adjusting these weights based on the data it processes.
4. Training the Model:
Deep learning models need training. It’s like teaching a computer to recognize patterns. You show it lots of examples, and it adjusts its internal settings (weights) to make predictions or classifications.
5. Application Examples:
Deep learning is used in many cool applications like image and speech recognition, language translation, playing games, and even in self-driving cars.
6. Why “Deep”?
The term “deep” comes from the multiple layers (depth) in these neural networks. The more layers, the more complex patterns the model can learn.
7. Challenges:
Training deep learning models can be resource-intensive, and sometimes it’s challenging to interpret how the model makes decisions (black box problem).
8. Real-World Project:
For a project, you might collect data, design a neural network, train it on the data, and then test its performance. It’s like teaching a computer to do a specific task by showing it examples.
TO THIS END, WE PROPOSE A BANK OF 3D-AWARE HIERARCHICAL FEATURES, INCLUDING GLOBAL, POINT-LEVEL, AND PIXEL-ALIGNED FEATURES, TO FACILITATE INFORMATIVE ENCODING.
BASED ON THIS OBSERVATION, WE PROPOSE A DATA SELECTOR BASED ON INSTAG TO SELECT 6K DIVERSE AND COMPLEX SAMPLES FROM OPEN-SOURCE DATASETS AND FINE-TUNE MODELS ON INSTAG-SELECTED DATA.
OUR APPROACH, NAMELY DIFFUSION-BASED CONDITIONAL INPAINTING FOR VIRTUAL TRY-ON (DCI-VTON), EFFECTIVELY UTILIZES THE POWER OF THE DIFFUSION MODEL, AND THE INCORPORATION OF THE WARPING MODULE HELPS TO PRODUCE HIGH-QUALITY AND REALISTIC VIRTUAL TRY-ON RESULTS.
IN THIS , WE DRAW INSPIRATION FROM ALBERTO ELFES' PIONEERING WORK IN 1989, WHERE HE INTRODUCED THE CONCEPT OF THE OCCUPANCY GRID AS WORLD MODELS FOR ROBOTS.
DATABASE ADMINISTRATORS (DBAS) PLAY A CRUCIAL ROLE IN MANAGING, MAINTAINING AND OPTIMIZING A DATABASE SYSTEM TO ENSURE DATA AVAILABILITY, PERFORMANCE, AND RELIABILITY.
IN THIS WORK, WE DEVELOP A UNIVERSAL SOLUTION TO VPR -- A TECHNIQUE THAT WORKS ACROSS A BROAD RANGE OF STRUCTURED AND UNSTRUCTURED ENVIRONMENTS (URBAN, OUTDOORS, INDOORS, AERIAL, UNDERWATER, AND SUBTERRANEAN ENVIRONMENTS) WITHOUT ANY RE-TRAINING OR FINE-TUNING.
IN THIS WORK, WE DEVELOP AND RELEASE LLAMA 2, A COLLECTION OF PRETRAINED AND FINE-TUNED LARGE LANGUAGE MODELS (LLMS) RANGING IN SCALE FROM 7 BILLION TO 70 BILLION PARAMETERS.
SYNTHETIC IMAGE DATASETS OFFER UNMATCHED ADVANTAGES FOR DESIGNING AND EVALUATING DEEP NEURAL NETWORKS: THEY MAKE IT POSSIBLE TO (I) RENDER AS MANY DATA SAMPLES AS NEEDED, (II) PRECISELY CONTROL EACH SCENE AND YIELD GRANULAR GROUND TRUTH LABELS (AND CAPTIONS), (III) PRECISELY CONTROL DISTRIBUTION SHIFTS BETWEEN TRAINING AND TESTING TO ISOLATE VARIABLES OF INTEREST FOR SOUND EXPERIMENTATION.
DIFFERENT FROM THE PREVIOUS SELF-KNOWLEDGE DISTILLATION, THIS STAGE FINETUNES THE STUDENT'S HEAD WITH ONLY 20% TRAINING TIME AS A PLUG-AND-PLAY TRAINING STRATEGY.
WE, FOR THE FIRST TIME, PROPOSE AN IMAGE RETRIEVAL PARADIGM LEVERAGING GLOBAL FEATURE ONLY TO ENABLE ACCURATE AND LIGHTWEIGHT IMAGE RETRIEVAL FOR BOTH COARSE RETRIEVAL AND RERANKING, THUS THE NAME - SUPERGLOBAL.
THIS EVOLUTION REQUIRES A COMBINATION OF BOTH TRADITIONAL METHODS (SUCH AS TERM-BASED SPARSE RETRIEVAL METHODS WITH RAPID RESPONSE) AND MODERN NEURAL ARCHITECTURES (SUCH AS LANGUAGE MODELS WITH POWERFUL LANGUAGE UNDERSTANDING CAPACITY).
LARGE LANGUAGE MODELS (LLMS) USUALLY SUFFER FROM KNOWLEDGE CUTOFF OR FALLACY ISSUES, WHICH MEANS THEY ARE UNAWARE OF UNSEEN EVENTS OR GENERATE TEXT WITH INCORRECT FACTS OWING TO THE OUTDATED/NOISY DATA.
MESH IS EXTRACTED FROM THE SIGNED DISTANCE FUNCTION (SDF) NETWORK FOR THE SURFACE, AND COLOR FOR EACH SURFACE VERTEX IS DRAWN FROM THE GLOBAL COLOR NETWORK.
WE INTRODUCE MUAVIC, A MULTILINGUAL AUDIO-VISUAL CORPUS FOR ROBUST SPEECH RECOGNITION AND ROBUST SPEECH-TO-TEXT TRANSLATION PROVIDING 1200 S OF AUDIO-VISUAL SPEECH IN 9 LANGUAGES.