CNNs are a type of neural network that can analyze visual imagery. They use convolutional and pooling layers to automatically extract image features and classify images. CNNs use convolution operations instead of general matrix multiplication, which allows them to identify important characteristics in images. They have sparse connectivity and parameter sharing that make them effective for tasks like computer vision, NLP, and audio analysis. Transfer learning is commonly used with CNNs to repurpose pre-trained models for new problems by retraining only the final layers.

1. WHAT IS CNN?
• a class of feed forward artificial neural networks that are applied to analyzing
visual imagery.
• CNN is designed to automatically and adaptively learn spatial hierarchies of
features through backpropagation by using multiple building blocks, such as
convolution layers, pooling layers, and fully connected layers.
• It’s neural networks that use convolution instead of general matrix multiplication
in at least one of their layers
• BY USING CONVOLUTION:
WE CAN REPRESENT THE INPUT SIGNAL WITH A NEW
REPRESENTATION THE MOST IMPORTANT CHARACTERISTIC OF GIVEN SIGNAL
BECOME VERY OBVIOUS
IN THIS NEW REPRESENTATION

2. “MOTIVATION”

3. DETECTION AND CLASSIFICATIONS TASKS
CV
NLP(TEXT)
AUDIO

4. CNN ARCHITECTURE PARTS:
• CONVOLUTIONAL BASE:
IS COMPOSED BY :A STACK OF CONVOLUTIONAL
AND POOLING LAYERS.
THE MAIN GOAL : TO GENERATE FEATURES FROM
THE IMAGE.
• CLASSIFIER:
IS COMPOSED BY: USUALLY FULLY CONNECTED
LAYERS.
THE MAIN GOAL :TO CLASSIFY THE IMAGE BASED
ON THE DETECTED FEATURES.

5. WHY CNN?

6. SPARSE
CONNECTIVITY
•

7. PARAMETE
R
SHARING

8. TRANSFER
LEARNING

9. * DEEP NETWORKS HAVE A LARGE NUMBER OF UNKNOWN
PARAMETERS ( IN MILLIONS ).
THE TASK OF TRAINING A NETWORK IS TO FIND THE
OPTIMUM PARAMETERS USING THE TRAINING DATA
* IF WE HAVE VERY FEW DATA, WE WILL GET ONLY
APPROXIMATE VALUES FOR MOST OF THE PARAMETERS,
WHICH WE DON’T WANT
For Deep Networks – More data -> Better
learning
Learning:

10. • IT IS DIFFICULT TO GET SUCH HUGE LABELED
DATASETS FOR TRAINING THE NETWORK.
• EVEN IF YOU GET THE DATA, IT TAKES A LARGE
AMOUNT OF TIME TO TRAIN THE NETWORK (
HUNDREDS OF HOURS ). THUS, IT TAKES A LOT OF
TIME, MONEY AND EFFORT TO TRAIN A DEEP
NETWORK SUCCESSFULLY.
The
problem

11. Solution
Transfer learning
Transfer of learning is the ability to
apply
knowledge learned in one context to
new contexts.
Variations:
● Same domain, different task
● Different domain, same task

12. • instead of starting learning process on model from scratch (with
random initialization), start from network that have been
learned when solving a different problem task.
• HOW TO REPURPOSING A PRE-TRAINED MODEL FOR YOUR OWN
NEEDS?
1) start by removing the original classifier(“off-the-shelf”)
2) then add a new classifier that fits your purposes.
3) finally train a new model on off-the-shelf features according to
one of three strategies( fine-tune )
Transfer
learning : idea

14. FINE-TUNING: SUPERVISED TASK
ADAPTATION
FINE-TUNE NETWORK USING
BACKPROP WITH LABELS FOR
TARGET DOMAIN UNTIL
VALIDATION LOSS STARTS TO
INCREASE

15. TRANSFER LEARNING SCENARIOS:

16. SIMILARITY MATRIX
CLASSIFY YOUR PROBLEM
ACCORDING TO THE SIZE OF DATA
this matrix classifies your computer vision problem considering the
size of your dataset and its similarity to the dataset in which your pre-
trained model was trained.

18. KERAS PRE-
TRAINED
IMAGENET
MODELS

19. WHAT IS IMAGENET
IMAGENET is a project which aims to provide a large image database for research
purposes. it contains more than 14 million images which belong to more than
20,000 classes ( or synsets ). they also provide bounding box annotations for
around 1 million images, which can be used in object localization tasks.

20. KERAS PRETRAINED MODELS
• keras comes bundled with many models.
• a trained model has two parts – model architecture and model weights. the
weights are large files and thus they are not bundled with keras.
MODELS:
• VGG16
• INCEPTIONV3
• RESNET50
• MOBILENET
• INCEPTIONRESNETV2

21. FUTURE WORK
• IN DEEP LEARNING:
While not perfect, such an approach works indeed. There is, though, lots of
improvements to be made:
* COLLECT MORE DATA: convnets require lots of data, for good
performance.
* SUITABLE LOSS FUNCTION: for simplicity used categorical cross-entropy
loss that works out- of-box.
will switch to a more suitable loss function, the one that better leverages
intra-class variance. good option to start with might have been a triplet loss
(see facenet paper for details).
* BETTER OVERALL CLASSIFIER ARCHITECTURE:
the current classifier is basically a prototype which goal is to switch to r-
cnn architecture.